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Dungeon of the Endless 1.1.5 Crack Mac Osx: How to Download and Play the Ultimate Dungeon Crawler
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If you are looking for a game that will test your skills and strategy, Dungeon of the Endless 1.1.5 Crack Mac Osx is a perfect choice. In this article, we will show you how to download and play this game on your Mac computer.
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How to Play Dungeon of the Endless 1.1.5 Crack Mac Osx
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- Escape safely: Your goal is to find the exit on each floor and reach it with at least one character carrying the crystal that powers your ship. You need to protect your crystal from enemies while moving it from room to room. -
Conclusion
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-Dungeon of the Endless 1.1.5 Crack Mac Osx has a lot of characters to choose from, each with their own stats, skills, and abilities. However, some of them are hidden and can only be unlocked by certain methods or conditions.
-If you want to unlock all the secret characters in Dungeon of the Endless 1.1.5 Crack Mac Osx, you can use a mod called Secret Unlocker (DotE-Secrets) v.1.1.5, which is a patch that adds them to the character selection screen. You can download it from gamepressure.com or other websites that offer mods for Dungeon of the Endless.
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- Download the mod file from gamepressure.com or other websites that offer mods for Dungeon of the Endless. -
- Copy the mod file to DungeonoftheEndless_Data\\Managed inside your game folder. -
- Run the installer and it will rename your original Assembly-CSharp.dll file to Assembly-CSharp.dll.backup. -
- Launch the game and you will see all the secret characters available on the character selection screen. -
Here are the secret characters that you can unlock with Secret Unlocker (DotE-Secrets) v.1.1.5:
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- Ayairi Whairydd (War Pug): A cute but fierce dog that can bite enemies and heal allies. -
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How to Install Mods for Dungeon of the Endless 1.1.5 Crack Mac Osx
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- Launch the game and enjoy the modded features or content. -
Here are some examples of mods that you can install for Dungeon of the Endless 1.1.5 Crack Mac Osx:
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How to Update Dungeon of the Endless 1.1.5 Crack Mac Osx
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How to Fix Common Problems with Dungeon of the Endless 1.1.5 Crack Mac Osx
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- Make sure your Mac computer meets the minimum system requirements for Dungeon of the Endless 1.1.5 Crack Mac Osx, such as operating system, processor, memory, graphics card, etc. -
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How to Customize Your Characters with Dungeon of the Endless 1.1.5 Crack Mac Osx
-Dungeon of the Endless 1.1.5 Crack Mac Osx has a lot of characters to choose from, each with their own stats, skills, and abilities. However, you can also customize your characters with various items, equipment, and mods that can enhance their performance and appearance.
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Here are some examples of items, equipment, and mods that you can use to customize your characters with Dungeon of the Endless 1.1.5 Crack Mac Osx:
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How to Enjoy Dungeon of the Endless 1.1.5 Crack Mac Osx with Your Friends
-Dungeon of the Endless 1.1.5 Crack Mac Osx is a game that can be played solo or with up to three other players online or locally. Playing with your friends can make the game more fun and challenging, as you can cooperate and communicate with each other to survive the endless dungeon.
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Conclusion
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-If you are looking for a game that will test your skills and strategy, Dungeon of the Endless 1.1.5 Crack Mac Osx is a perfect choice. You can download it for free from various websites that offer cracked games for Mac users, such as kidzshare.com or trailduro.com.
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Buble Shooter Join the Bubble Popping Adventure.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Buble Shooter Join the Bubble Popping Adventure.md deleted file mode 100644 index 539f3821c6169b386a9114b47b5d1873f70e7808..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Buble Shooter Join the Bubble Popping Adventure.md +++ /dev/null @@ -1,123 +0,0 @@ -
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Bubble Shooter: A Fun and Addictive Game for Everyone
-If you are looking for a simple yet entertaining game to pass the time, you might want to try Bubble Shooter. Bubble Shooter is a popular online game that involves shooting bubbles to match three or more of the same color and make them pop. It is easy to learn, fun to play, and suitable for all ages. In this article, we will tell you everything you need to know about Bubble Shooter, including its history, rules, benefits, tips, and best versions.
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The history of Bubble Shooter
-Bubble Shooter was originally developed by Taito Corporation in 1994 as an arcade game called Puzzle Bobble. It was later ported to various platforms such as PC, mobile, and web browsers. The game became very popular and spawned many sequels and spin-offs. One of the most successful versions of the game was Bubble Shooter, which was released in 2002 by Absolutist Games. This version introduced some new features such as power-ups, levels, and modes. Since then, Bubble Shooter has been played by millions of people around the world and has inspired many other similar games.
-The rules of Bubble Shooter
-The rules of Bubble Shooter are simple and intuitive. Here are the basic steps to play the game:
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- At the bottom of the screen, you will see a bubble cannon that can be moved left and right with your mouse or finger. -
- At the center of the cannon, you will see a bubble that is ready to be launched. You can also see the next bubble that will come after it. -
- Aim the cannon at the cluster of bubbles at the top of the screen. You can see where the bubble will go by following the dotted line. -
- Click or tap to shoot the bubble. Try to hit bubbles of the same color as your bubble. -
- If you manage to connect three or more bubbles of the same color, they will pop and disappear. You will also earn points for each bubble you pop. -
- If you miss or hit a different color, the bubble will stick to the cluster and make it bigger. -
- If you pop all the bubbles on the screen, you will complete the level and move on to the next one. -
- If one of the bubbles touches the bottom of the screen, you will lose a life and have to start over. -
The benefits of playing Bubble Shooter
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- It improves your concentration and focus. You have to pay attention to the colors, angles, and trajectories of the bubbles. -
- It enhances your hand-eye coordination and reaction time. You have to move quickly and accurately to shoot the bubbles. -
- It stimulates your brain and memory. You have to plan ahead and remember where the bubbles are located and how to pop them. -
- It relaxes your mind and mood. You can enjoy the colorful graphics, the soothing sounds, and the satisfying feeling of popping bubbles. -
- It challenges your skills and creativity. You can try different strategies and techniques to beat the levels and score higher. -
How to play Bubble Shooter?
-Now that you know what Bubble Shooter is and why you should play it, let's see how you can actually play it. Here are some tips and tricks to help you master the game:
-Choose your device and platform
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-Aim and shoot the bubbles
-The most important skill in Bubble Shooter is aiming and shooting the bubbles. You need to be precise and accurate to hit the right bubbles and avoid wasting shots. Here are some tips to improve your aiming and shooting:
--
-
- Use the dotted line as a guide. The dotted line shows you where the bubble will go when you shoot it. You can adjust the angle of the cannon by moving your mouse or finger. -
- Use the walls as a bounce. The walls can help you reach bubbles that are hard to hit directly. You can bounce the bubble off the wall and make it ricochet to the target. -
- Use the color of the next bubble as a hint. The color of the next bubble shows you what color will come after the current one. You can use this information to plan ahead and prepare for your next shot. -
Use strategies and tips to improve your score
-Besides aiming and shooting, there are also some strategies and tips that can help you improve your score and beat the levels. Here are some of them:
--
-
- Pop more bubbles with one shot. The more bubbles you pop with one shot, the more points you get. You can also trigger chain reactions and combos by popping bubbles that are connected to other bubbles of the same color. -
- Clear the top rows first. The top rows are more dangerous because they are closer to the bottom of the screen. If you clear them first, you will have more space and time to shoot the lower rows. -
- Avoid creating isolated bubbles. Isolated bubbles are bubbles that are not connected to any other bubbles of the same color. They are harder to pop and waste your shots. Try to avoid creating them by shooting at groups of bubbles instead of single ones. -
- Use power-ups wisely. Power-ups are special bubbles that have different effects when popped. For example, some power-ups can clear a whole row or column of bubbles, change the color of nearby bubbles, or give you extra lives or shots. Use them wisely when you need them, but don't rely on them too much. -
What are the best Bubble Shooter games?
-Bubble Shooter is a very popular game that has many versions and variations. Some of them are more classic and simple, while others are more modern and complex. Here are some of the best Bubble Shooter games that you can try:
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-Conclusion
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-FAQs
-Here are some of the frequently asked questions about Bubble Shooter:
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- Q: How many levels are there in Bubble Shooter? -
- A: The number of levels in Bubble Shooter depends on the version and platform you are playing on. Some versions have a fixed number of levels, while others have an infinite number of levels that are randomly generated. You can check the level number on the screen or the menu. -
- Q: How do I get more lives or shots in Bubble Shooter? -
- A: The number of lives or shots in Bubble Shooter also depends on the version and platform you are playing on. Some versions have a limited number of lives or shots that you can replenish by watching ads, buying coins, or waiting for a certain time. Other versions have an unlimited number of lives or shots that you can use freely. -
- Q: How do I pause or resume the game in Bubble Shooter? -
- A: To pause or resume the game in Bubble Shooter, you can click or tap on the pause button on the screen or the menu. This will stop the game and allow you to access other options such as settings, sound, music, help, or exit. -
- Q: How do I save or load my progress in Bubble Shooter? -
- A: To save or load your progress in Bubble Shooter, you need to have an account or a profile on the platform you are playing on. This will allow you to sync your data across different devices and resume your game from where you left off. -
- Q: How do I change the difficulty or mode in Bubble Shooter? -
- A: To change the difficulty or mode in Bubble Shooter, you can select the option on the screen or the menu before starting a new game. Some versions have different difficulty levels such as easy, medium, hard, or expert. Other versions have different modes such as arcade, puzzle, time trial, or survival. -
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\ No newline at end of file diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Countries.csv The Ultimate Resource for Country Information.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Countries.csv The Ultimate Resource for Country Information.md deleted file mode 100644 index ae208b74d5dfa0fdcf2e881d93b59e0814bb591d..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Countries.csv The Ultimate Resource for Country Information.md +++ /dev/null @@ -1,112 +0,0 @@ -
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How to Download Countries.csv
-A CSV file, or a comma-separated values file, is a plain text file that stores data in a tabular format. Each line of the file is a data record, and each record consists of one or more fields separated by commas. CSV files are often used to exchange data between different applications that use incompatible formats. For example, you can use a CSV file to transfer data from a database to a spreadsheet, or vice versa.
-One example of a CSV file that you might want to download is countries.csv. This file contains information about countries around the world, such as their names, ISO codes, coordinates, capitals, currencies, regions, and more. You can use this file for various purposes, such as creating maps, charts, reports, or quizzes. In this article, we will show you how to download countries.csv and open it in a program of your choice.
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Step 1: Find a Source of Countries.csv Data
-The first step is to find a reliable source of countries.csv data. There are many websites that offer this kind of data for free or for a fee. Some examples are:
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- Google Developers: This website provides a canonical version of countries.csv that follows the ISO 3166 standard for country codes. It also includes some additional fields, such as time zones, latitude, longitude, emoji, and native name. -
- GitHub: This website hosts a repository of countries, states, and cities data in JSON, SQL, XML, YAML, and CSV formats. The CSV files include an index column and are updated regularly. -
- Kaggle: This website offers a dataset of countries of the world that links country names to region, population, area size, GDP, mortality, and more. The dataset is available in CSV format and can be downloaded or accessed through an API. -
You can choose any source that suits your needs and preferences. For this article, we will use the Google Developers version of countries.csv.
-Step 2: Choose a Program to Open the CSV File
-The next step is to choose a program that can open and display the CSV file. There are many options available, depending on your operating system and your goals. Some common programs are:
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- Text editors: These are programs that allow you to view and edit plain text files. Examples are Notepad, Notepad++, Sublime Text, Atom, or Visual Studio Code. Text editors are useful for quickly viewing the contents of a CSV file or making minor changes. -
- Spreadsheet programs: These are programs that allow you to organize and analyze data in tabular form. Examples are Microsoft Excel, Google Sheets, LibreOffice Calc, or Numbers. Spreadsheet programs are useful for performing calculations, creating charts, filtering data, or applying formulas. -
- Specialized applications: These are programs that are designed for specific purposes or tasks related to CSV files. Examples are CSV Editor Pro, CSVed, Ron's Editor, or Easy Data Transform. Specialized applications are useful for editing large or complex CSV files, converting formats, validating data, or transforming data. -
You can choose any program that meets your requirements and expectations. For this article, we will use Microsoft Excel as an example of a spreadsheet program.
-Step 3: Download the CSV File from the Source
-The third step is to download the CSV file from the source website. To do this:
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-
-
- Go to the website where the CSV file is hosted. In our case, it is https://developers.google.com/public-data/docs/canonical/countries_csv. -
- Right-click on the link to the CSV file and select "Save link as" or "Save target as". In our case, it is https://developers.google.com/public-data/docs/canonical/countries_csv.csv. -
- Choose a location on your computer where you want to save the CSV file and click "Save". -
You have now downloaded the CSV file to your computer. You can find it in the location you specified.
-Step 4: Open the CSV File in the Chosen Program
-The fourth step is to open the CSV file in the program you selected. To do this:
--
-
- Launch the program on your computer. In our case, it is Microsoft Excel. -
- Click on "File" and then "Open". Alternatively, you can use the keyboard shortcut Ctrl+O. -
- Navigate to the location where you saved the CSV file and select it. Click "Open". -
You should now see the CSV file opened in the program. Depending on the program, you may need to adjust some settings, such as the delimiter, the encoding, or the format of the data. For example, in Excel, you may see a dialog box that asks you to choose how to import the data. You can select "Delimited" and then "Comma" as the delimiter. You can also choose the column data format as "General" or "Text". Click "Finish" to complete the import.
-Step 5: Explore and Manipulate the Data as Needed
-The final step is to explore and manipulate the data in the CSV file as needed. You can use the features and functions of the program to perform various tasks, such as:
--
-
- Sort and filter: You can sort and filter the data by any column or criteria. For example, you can sort the countries by name, population, or region. You can also filter out countries that meet certain conditions, such as having a specific currency or language. -
- Calculate and analyze: You can calculate and analyze the data using formulas, functions, or tools. For example, you can calculate the average population, area, or GDP of countries in a region. You can also use tools such as pivot tables or charts to summarize and visualize the data. -
- Edit and format: You can edit and format the data to suit your needs and preferences. For example, you can add, delete, or modify rows or columns of data. You can also change the font, color, or alignment of the cells. -
You can explore and manipulate the data in any way you want. You can also save your changes or export the data to another format if needed.
-Conclusion
-In this article, we have shown you how to download countries.csv and open it in a program of your choice. We have also given you some examples of how to explore and manipulate the data in the CSV file. By following these steps, you can access a wealth of information about countries around the world and use it for various purposes.
-We hope you found this article helpful and informative. If you have any questions or feedback, please let us know in the comments below.
-FAQs
-What is a CSV file?
-A CSV file is a plain text file that stores data in a tabular format. Each line of the file is a data record, and each record consists of one or more fields separated by commas.
-Why should I download countries.csv?
-You should download countries.csv if you want to access information about countries around the world, such as their names, ISO codes, coordinates, capitals, currencies, regions, and more. You can use this information for various purposes, such as creating maps, charts, reports, or quizzes.
-How do I open a CSV file?
-You can open a CSV file using any program that can read and display plain text files. Some common programs are text editors, spreadsheet programs, or specialized applications.
-Where can I find other sources of countries.csv data?
-You can find other sources of countries.csv data by searching online for websites that offer this kind of data for free or for a fee. Some examples are GitHub, Kaggle, DataHub.io, World Bank Data Catalogue, or CIA World Factbook.
-How do I convert a CSV file to another format?
-You can convert a CSV file to another format using any program that can read and write different formats. Some common formats are JSON, SQL, XML, YAML, or HTML. You can also use online tools or converters that can perform this task for you.
197e85843d-
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\ No newline at end of file diff --git a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/README.md b/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/README.md deleted file mode 100644 index 2ee63a861229b68873561fa39bfa7c9a8b53b947..0000000000000000000000000000000000000000 --- a/spaces/4Taps/SadTalker/src/face3d/models/arcface_torch/README.md +++ /dev/null @@ -1,164 +0,0 @@ -# Distributed Arcface Training in Pytorch - -This is a deep learning library that makes face recognition efficient, and effective, which can train tens of millions -identity on a single server. - -## Requirements - -- Install [pytorch](http://pytorch.org) (torch>=1.6.0), our doc for [install.md](docs/install.md). -- `pip install -r requirements.txt`. -- Download the dataset - from [https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_) - . - -## How to Training - -To train a model, run `train.py` with the path to the configs: - -### 1. Single node, 8 GPUs: - -```shell -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r50 -``` - -### 2. Multiple nodes, each node 8 GPUs: - -Node 0: - -```shell -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50 -``` - -Node 1: - -```shell -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50 -``` - -### 3.Training resnet2060 with 8 GPUs: - -```shell -python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r2060.py -``` - -## Model Zoo - -- The models are available for non-commercial research purposes only. -- All models can be found in here. -- [Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g): e8pw -- [onedrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d) - -### Performance on [**ICCV2021-MFR**](http://iccv21-mfr.com/) - -ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face -recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities. -As the result, we can evaluate the FAIR performance for different algorithms. - -For **ICCV2021-MFR-ALL** set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The -globalised multi-racial testset contains 242,143 identities and 1,624,305 images. - -For **ICCV2021-MFR-MASK** set, TAR is measured on mask-to-nonmask 1:1 protocal, with FAR less than 0.0001(e-4). -Mask testset contains 6,964 identities, 6,964 masked images and 13,928 non-masked images. -There are totally 13,928 positive pairs and 96,983,824 negative pairs. - -| Datasets | backbone | Training throughout | Size / MB | **ICCV2021-MFR-MASK** | **ICCV2021-MFR-ALL** | -| :---: | :--- | :--- | :--- |:--- |:--- | -| MS1MV3 | r18 | - | 91 | **47.85** | **68.33** | -| Glint360k | r18 | 8536 | 91 | **53.32** | **72.07** | -| MS1MV3 | r34 | - | 130 | **58.72** | **77.36** | -| Glint360k | r34 | 6344 | 130 | **65.10** | **83.02** | -| MS1MV3 | r50 | 5500 | 166 | **63.85** | **80.53** | -| Glint360k | r50 | 5136 | 166 | **70.23** | **87.08** | -| MS1MV3 | r100 | - | 248 | **69.09** | **84.31** | -| Glint360k | r100 | 3332 | 248 | **75.57** | **90.66** | -| MS1MV3 | mobilefacenet | 12185 | 7.8 | **41.52** | **65.26** | -| Glint360k | mobilefacenet | 11197 | 7.8 | **44.52** | **66.48** | - -### Performance on IJB-C and Verification Datasets - -| Datasets | backbone | IJBC(1e-05) | IJBC(1e-04) | agedb30 | cfp_fp | lfw | log | -| :---: | :--- | :--- | :--- | :--- |:--- |:--- |:--- | -| MS1MV3 | r18 | 92.07 | 94.66 | 97.77 | 97.73 | 99.77 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r18_fp16/training.log)| -| MS1MV3 | r34 | 94.10 | 95.90 | 98.10 | 98.67 | 99.80 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r34_fp16/training.log)| -| MS1MV3 | r50 | 94.79 | 96.46 | 98.35 | 98.96 | 99.83 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r50_fp16/training.log)| -| MS1MV3 | r100 | 95.31 | 96.81 | 98.48 | 99.06 | 99.85 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r100_fp16/training.log)| -| MS1MV3 | **r2060**| 95.34 | 97.11 | 98.67 | 99.24 | 99.87 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r2060_fp16/training.log)| -| Glint360k |r18-0.1 | 93.16 | 95.33 | 97.72 | 97.73 | 99.77 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r18_fp16_0.1/training.log)| -| Glint360k |r34-0.1 | 95.16 | 96.56 | 98.33 | 98.78 | 99.82 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r34_fp16_0.1/training.log)| -| Glint360k |r50-0.1 | 95.61 | 96.97 | 98.38 | 99.20 | 99.83 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r50_fp16_0.1/training.log)| -| Glint360k |r100-0.1 | 95.88 | 97.32 | 98.48 | 99.29 | 99.82 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r100_fp16_0.1/training.log)| - -[comment]: <> (More details see [model.md](docs/modelzoo.md) in docs.) - - -## [Speed Benchmark](docs/speed_benchmark.md) - -**Arcface Torch** can train large-scale face recognition training set efficiently and quickly. When the number of -classes in training sets is greater than 300K and the training is sufficient, partial fc sampling strategy will get same -accuracy with several times faster training performance and smaller GPU memory. -Partial FC is a sparse variant of the model parallel architecture for large sacle face recognition. Partial FC use a -sparse softmax, where each batch dynamicly sample a subset of class centers for training. In each iteration, only a -sparse part of the parameters will be updated, which can reduce a lot of GPU memory and calculations. With Partial FC, -we can scale trainset of 29 millions identities, the largest to date. Partial FC also supports multi-machine distributed -training and mixed precision training. - - - -More details see -[speed_benchmark.md](docs/speed_benchmark.md) in docs. - -### 1. Training speed of different parallel methods (samples / second), Tesla V100 32GB * 8. (Larger is better) - -`-` means training failed because of gpu memory limitations. - -| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 | -| :--- | :--- | :--- | :--- | -|125000 | 4681 | 4824 | 5004 | -|1400000 | **1672** | 3043 | 4738 | -|5500000 | **-** | **1389** | 3975 | -|8000000 | **-** | **-** | 3565 | -|16000000 | **-** | **-** | 2679 | -|29000000 | **-** | **-** | **1855** | - -### 2. GPU memory cost of different parallel methods (MB per GPU), Tesla V100 32GB * 8. (Smaller is better) - -| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 | -| :--- | :--- | :--- | :--- | -|125000 | 7358 | 5306 | 4868 | -|1400000 | 32252 | 11178 | 6056 | -|5500000 | **-** | 32188 | 9854 | -|8000000 | **-** | **-** | 12310 | -|16000000 | **-** | **-** | 19950 | -|29000000 | **-** | **-** | 32324 | - -## Evaluation ICCV2021-MFR and IJB-C - -More details see [eval.md](docs/eval.md) in docs. - -## Test - -We tested many versions of PyTorch. Please create an issue if you are having trouble. - -- [x] torch 1.6.0 -- [x] torch 1.7.1 -- [x] torch 1.8.0 -- [x] torch 1.9.0 - -## Citation - -``` -@inproceedings{deng2019arcface, - title={Arcface: Additive angular margin loss for deep face recognition}, - author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos}, - booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, - pages={4690--4699}, - year={2019} -} -@inproceedings{an2020partical_fc, - title={Partial FC: Training 10 Million Identities on a Single Machine}, - author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and - Zhang, Debing and Fu Ying}, - booktitle={Arxiv 2010.05222}, - year={2020} -} -``` diff --git a/spaces/4Taps/SadTalker/src/utils/audio.py b/spaces/4Taps/SadTalker/src/utils/audio.py deleted file mode 100644 index 89433eb4c681112804fbed72b157700f553739a8..0000000000000000000000000000000000000000 --- a/spaces/4Taps/SadTalker/src/utils/audio.py +++ /dev/null @@ -1,136 +0,0 @@ -import librosa -import librosa.filters -import numpy as np -# import tensorflow as tf -from scipy import signal -from scipy.io import wavfile -from src.utils.hparams import hparams as hp - -def load_wav(path, sr): - return librosa.core.load(path, sr=sr)[0] - -def save_wav(wav, path, sr): - wav *= 32767 / max(0.01, np.max(np.abs(wav))) - #proposed by @dsmiller - wavfile.write(path, sr, wav.astype(np.int16)) - -def save_wavenet_wav(wav, path, sr): - librosa.output.write_wav(path, wav, sr=sr) - -def preemphasis(wav, k, preemphasize=True): - if preemphasize: - return signal.lfilter([1, -k], [1], wav) - return wav - -def inv_preemphasis(wav, k, inv_preemphasize=True): - if inv_preemphasize: - return signal.lfilter([1], [1, -k], wav) - return wav - -def get_hop_size(): - hop_size = hp.hop_size - if hop_size is None: - assert hp.frame_shift_ms is not None - hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate) - return hop_size - -def linearspectrogram(wav): - D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) - S = _amp_to_db(np.abs(D)) - hp.ref_level_db - - if hp.signal_normalization: - return _normalize(S) - return S - -def melspectrogram(wav): - D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) - S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db - - if hp.signal_normalization: - return _normalize(S) - return S - -def _lws_processor(): - import lws - return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech") - -def _stft(y): - if hp.use_lws: - return _lws_processor(hp).stft(y).T - else: - return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size) - -########################################################## -#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) -def num_frames(length, fsize, fshift): - """Compute number of time frames of spectrogram - """ - pad = (fsize - fshift) - if length % fshift == 0: - M = (length + pad * 2 - fsize) // fshift + 1 - else: - M = (length + pad * 2 - fsize) // fshift + 2 - return M - - -def pad_lr(x, fsize, fshift): - """Compute left and right padding - """ - M = num_frames(len(x), fsize, fshift) - pad = (fsize - fshift) - T = len(x) + 2 * pad - r = (M - 1) * fshift + fsize - T - return pad, pad + r -########################################################## -#Librosa correct padding -def librosa_pad_lr(x, fsize, fshift): - return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] - -# Conversions -_mel_basis = None - -def _linear_to_mel(spectogram): - global _mel_basis - if _mel_basis is None: - _mel_basis = _build_mel_basis() - return np.dot(_mel_basis, spectogram) - -def _build_mel_basis(): - assert hp.fmax <= hp.sample_rate // 2 - return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels, - fmin=hp.fmin, fmax=hp.fmax) - -def _amp_to_db(x): - min_level = np.exp(hp.min_level_db / 20 * np.log(10)) - return 20 * np.log10(np.maximum(min_level, x)) - -def _db_to_amp(x): - return np.power(10.0, (x) * 0.05) - -def _normalize(S): - if hp.allow_clipping_in_normalization: - if hp.symmetric_mels: - return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value, - -hp.max_abs_value, hp.max_abs_value) - else: - return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value) - - assert S.max() <= 0 and S.min() - hp.min_level_db >= 0 - if hp.symmetric_mels: - return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value - else: - return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)) - -def _denormalize(D): - if hp.allow_clipping_in_normalization: - if hp.symmetric_mels: - return (((np.clip(D, -hp.max_abs_value, - hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) - + hp.min_level_db) - else: - return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) - - if hp.symmetric_mels: - return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db) - else: - return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) diff --git a/spaces/7eu7d7/anime-ai-detect-fucker/attacker/__init__.py b/spaces/7eu7d7/anime-ai-detect-fucker/attacker/__init__.py deleted file mode 100644 index 7530520945ce3b7e63f5c24ef9e1093dd7dcb431..0000000000000000000000000000000000000000 --- a/spaces/7eu7d7/anime-ai-detect-fucker/attacker/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .base import * -from .PGD import * -from .FGSM import * \ No newline at end of file diff --git a/spaces/AIFILMS/StyleGANEX/models/stylegan2/lpips/base_model.py b/spaces/AIFILMS/StyleGANEX/models/stylegan2/lpips/base_model.py deleted file mode 100644 index 8de1d16f0c7fa52d8067139abc6e769e96d0a6a1..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/StyleGANEX/models/stylegan2/lpips/base_model.py +++ /dev/null @@ -1,58 +0,0 @@ -import os -import numpy as np -import torch -from torch.autograd import Variable -from pdb import set_trace as st -from IPython import embed - -class BaseModel(): - def __init__(self): - pass; - - def name(self): - return 'BaseModel' - - def initialize(self, use_gpu=True, gpu_ids=[0]): - self.use_gpu = use_gpu - self.gpu_ids = gpu_ids - - def forward(self): - pass - - def get_image_paths(self): - pass - - def optimize_parameters(self): - pass - - def get_current_visuals(self): - return self.input - - def get_current_errors(self): - return {} - - def save(self, label): - pass - - # helper saving function that can be used by subclasses - def save_network(self, network, path, network_label, epoch_label): - save_filename = '%s_net_%s.pth' % (epoch_label, network_label) - save_path = os.path.join(path, save_filename) - torch.save(network.state_dict(), save_path) - - # helper loading function that can be used by subclasses - def load_network(self, network, network_label, epoch_label): - save_filename = '%s_net_%s.pth' % (epoch_label, network_label) - save_path = os.path.join(self.save_dir, save_filename) - print('Loading network from %s'%save_path) - network.load_state_dict(torch.load(save_path)) - - def update_learning_rate(): - pass - - def get_image_paths(self): - return self.image_paths - - def save_done(self, flag=False): - np.save(os.path.join(self.save_dir, 'done_flag'),flag) - np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i') diff --git a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/scheduler.py b/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/scheduler.py deleted file mode 100644 index 7151ffbab25a113673b7627027b443b27f22cb0f..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/training/scheduler.py +++ /dev/null @@ -1,24 +0,0 @@ -import numpy as np - - -def assign_learning_rate(optimizer, new_lr): - for param_group in optimizer.param_groups: - param_group["lr"] = new_lr - - -def _warmup_lr(base_lr, warmup_length, step): - return base_lr * (step + 1) / warmup_length - - -def cosine_lr(optimizer, base_lr, warmup_length, steps): - def _lr_adjuster(step): - if step < warmup_length: - lr = _warmup_lr(base_lr, warmup_length, step) - else: - e = step - warmup_length - es = steps - warmup_length - lr = 0.5 * (1 + np.cos(np.pi * e / es)) * base_lr - assign_learning_rate(optimizer, lr) - return lr - - return _lr_adjuster diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/vocoder/bigvgan/models.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/vocoder/bigvgan/models.py deleted file mode 100644 index 22e8017b6d70c8399b3be6a2555485634c03e72d..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/vocoder/bigvgan/models.py +++ /dev/null @@ -1,414 +0,0 @@ -# Copyright (c) 2022 NVIDIA CORPORATION. -# Licensed under the MIT license. - -# Adapted from https://github.com/jik876/hifi-gan under the MIT license. -# LICENSE is in incl_licenses directory. - - -import torch -import torch.nn.functional as F -import torch.nn as nn -from torch.nn import Conv1d, ConvTranspose1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -import numpy as np -from .activations import Snake,SnakeBeta -from .alias_free_torch import * -import os -from omegaconf import OmegaConf - -LRELU_SLOPE = 0.1 - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size*dilation - dilation)/2) - -class AMPBlock1(torch.nn.Module): - def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None): - super(AMPBlock1, self).__init__() - self.h = h - - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers - - if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing - self.activations = nn.ModuleList([ - Activation1d( - activation=Snake(channels, alpha_logscale=h.snake_logscale)) - for _ in range(self.num_layers) - ]) - elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing - self.activations = nn.ModuleList([ - Activation1d( - activation=SnakeBeta(channels, alpha_logscale=h.snake_logscale)) - for _ in range(self.num_layers) - ]) - else: - raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.") - - def forward(self, x): - acts1, acts2 = self.activations[::2], self.activations[1::2] - for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2): - xt = a1(x) - xt = c1(xt) - xt = a2(xt) - xt = c2(xt) - x = xt + x - - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class AMPBlock2(torch.nn.Module): - def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None): - super(AMPBlock2, self).__init__() - self.h = h - - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - self.num_layers = len(self.convs) # total number of conv layers - - if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing - self.activations = nn.ModuleList([ - Activation1d( - activation=Snake(channels, alpha_logscale=h.snake_logscale)) - for _ in range(self.num_layers) - ]) - elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing - self.activations = nn.ModuleList([ - Activation1d( - activation=SnakeBeta(channels, alpha_logscale=h.snake_logscale)) - for _ in range(self.num_layers) - ]) - else: - raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.") - - def forward(self, x): - for c, a in zip (self.convs, self.activations): - xt = a(x) - xt = c(xt) - x = xt + x - - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class BigVGAN(torch.nn.Module): - # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks. - def __init__(self, h): - super(BigVGAN, self).__init__() - self.h = h - - self.num_kernels = len(h.resblock_kernel_sizes) - self.num_upsamples = len(h.upsample_rates) - - # pre conv - self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)) - - # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default - resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2 - - # transposed conv-based upsamplers. does not apply anti-aliasing - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): - self.ups.append(nn.ModuleList([ - weight_norm(ConvTranspose1d(h.upsample_initial_channel // (2 ** i), - h.upsample_initial_channel // (2 ** (i + 1)), - k, u, padding=(k - u) // 2)) - ])) - - # residual blocks using anti-aliased multi-periodicity composition modules (AMP) - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = h.upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): - self.resblocks.append(resblock(h, ch, k, d, activation=h.activation)) - - # post conv - if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing - activation_post = Snake(ch, alpha_logscale=h.snake_logscale) - self.activation_post = Activation1d(activation=activation_post) - elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing - activation_post = SnakeBeta(ch, alpha_logscale=h.snake_logscale) - self.activation_post = Activation1d(activation=activation_post) - else: - raise NotImplementedError("activation incorrectly specified. check the config file and look for 'activation'.") - - self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) - - # weight initialization - for i in range(len(self.ups)): - self.ups[i].apply(init_weights) - self.conv_post.apply(init_weights) - - def forward(self, x): - # pre conv - x = self.conv_pre(x) - - for i in range(self.num_upsamples): - # upsampling - for i_up in range(len(self.ups[i])): - x = self.ups[i][i_up](x) - # AMP blocks - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - - # post conv - x = self.activation_post(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - for l_i in l: - remove_weight_norm(l_i) - for l in self.resblocks: - l.remove_weight_norm() - remove_weight_norm(self.conv_pre) - remove_weight_norm(self.conv_post) - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, h, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.d_mult = h.discriminator_channel_mult - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, int(32*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(int(32*self.d_mult), int(128*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(int(128*self.d_mult), int(512*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(int(512*self.d_mult), int(1024*self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), - norm_f(Conv2d(int(1024*self.d_mult), int(1024*self.d_mult), (kernel_size, 1), 1, padding=(2, 0))), - ]) - self.conv_post = norm_f(Conv2d(int(1024*self.d_mult), 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, h): - super(MultiPeriodDiscriminator, self).__init__() - self.mpd_reshapes = h.mpd_reshapes - print("mpd_reshapes: {}".format(self.mpd_reshapes)) - discriminators = [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes] - self.discriminators = nn.ModuleList(discriminators) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorR(nn.Module): - def __init__(self, cfg, resolution): - super().__init__() - - self.resolution = resolution - assert len(self.resolution) == 3, \ - "MRD layer requires list with len=3, got {}".format(self.resolution) - self.lrelu_slope = LRELU_SLOPE - - norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm - if hasattr(cfg, "mrd_use_spectral_norm"): - print("INFO: overriding MRD use_spectral_norm as {}".format(cfg.mrd_use_spectral_norm)) - norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm - self.d_mult = cfg.discriminator_channel_mult - if hasattr(cfg, "mrd_channel_mult"): - print("INFO: overriding mrd channel multiplier as {}".format(cfg.mrd_channel_mult)) - self.d_mult = cfg.mrd_channel_mult - - self.convs = nn.ModuleList([ - norm_f(nn.Conv2d(1, int(32*self.d_mult), (3, 9), padding=(1, 4))), - norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))), - norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))), - norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))), - norm_f(nn.Conv2d(int(32*self.d_mult), int(32*self.d_mult), (3, 3), padding=(1, 1))), - ]) - self.conv_post = norm_f(nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1))) - - def forward(self, x): - fmap = [] - - x = self.spectrogram(x) - x = x.unsqueeze(1) - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, self.lrelu_slope) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - def spectrogram(self, x): - n_fft, hop_length, win_length = self.resolution - x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect') - x = x.squeeze(1) - x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=True) - x = torch.view_as_real(x) # [B, F, TT, 2] - mag = torch.norm(x, p=2, dim =-1) #[B, F, TT] - - return mag - - -class MultiResolutionDiscriminator(nn.Module): - def __init__(self, cfg, debug=False): - super().__init__() - self.resolutions = cfg.resolutions - assert len(self.resolutions) == 3,\ - "MRD requires list of list with len=3, each element having a list with len=3. got {}".\ - format(self.resolutions) - self.discriminators = nn.ModuleList( - [DiscriminatorR(cfg, resolution) for resolution in self.resolutions] - ) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(x=y) - y_d_g, fmap_g = d(x=y_hat) - y_d_rs.append(y_d_r) - fmap_rs.append(fmap_r) - y_d_gs.append(y_d_g) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -def feature_loss(fmap_r, fmap_g): - loss = 0 - for dr, dg in zip(fmap_r, fmap_g): - for rl, gl in zip(dr, dg): - loss += torch.mean(torch.abs(rl - gl)) - - return loss*2 - - -def discriminator_loss(disc_real_outputs, disc_generated_outputs): - loss = 0 - r_losses = [] - g_losses = [] - for dr, dg in zip(disc_real_outputs, disc_generated_outputs): - r_loss = torch.mean((1-dr)**2) - g_loss = torch.mean(dg**2) - loss += (r_loss + g_loss) - r_losses.append(r_loss.item()) - g_losses.append(g_loss.item()) - - return loss, r_losses, g_losses - - -def generator_loss(disc_outputs): - loss = 0 - gen_losses = [] - for dg in disc_outputs: - l = torch.mean((1-dg)**2) - gen_losses.append(l) - loss += l - - return loss, gen_losses - - - -class VocoderBigVGAN(object): - def __init__(self, ckpt_vocoder,device='cuda'): - vocoder_sd = torch.load(os.path.join(ckpt_vocoder,'best_netG.pt'), map_location='cpu') - - vocoder_args = OmegaConf.load(os.path.join(ckpt_vocoder,'args.yml')) - - self.generator = BigVGAN(vocoder_args) - self.generator.load_state_dict(vocoder_sd['generator']) - self.generator.eval() - - self.device = device - self.generator.to(self.device) - - def vocode(self, spec): - with torch.no_grad(): - if isinstance(spec,np.ndarray): - spec = torch.from_numpy(spec).unsqueeze(0) - spec = spec.to(dtype=torch.float32,device=self.device) - return self.generator(spec).squeeze().cpu().numpy() - - def __call__(self, wav): - return self.vocode(wav) diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/mobilevit-small_4xb32_2000e_3c_noF/__init__.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/mobilevit-small_4xb32_2000e_3c_noF/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/AUBADA-ALARABI/poetry202/README.md b/spaces/AUBADA-ALARABI/poetry202/README.md deleted file mode 100644 index b19a751ca3854ec2c2b7ac5c56aece4cc38657c6..0000000000000000000000000000000000000000 --- a/spaces/AUBADA-ALARABI/poetry202/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Poetry2023 -emoji: 👁 -colorFrom: green -colorTo: gray -sdk: gradio -sdk_version: 3.16.0 -app_file: app.py -pinned: false -duplicated_from: Abdllh/poetry202 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Abhi5ingh/fashionsd/sdfile.py b/spaces/Abhi5ingh/fashionsd/sdfile.py deleted file mode 100644 index ad62243e77d18ca26e80dcf8263c80f746c76302..0000000000000000000000000000000000000000 --- a/spaces/Abhi5ingh/fashionsd/sdfile.py +++ /dev/null @@ -1,89 +0,0 @@ -import gc -import datetime -import os -import re -from typing import Literal - -import streamlit as st -import torch -from diffusers import ( - StableDiffusionPipeline, - StableDiffusionControlNetPipeline, - ControlNetModel, - EulerDiscreteScheduler, - DDIMScheduler, -) - -PIPELINES = Literal["txt2img", "sketch2img"] - -@st.cache_resource(max_entries=1) -def get_pipelines( name:PIPELINES, enable_cpu_offload = False, ) -> StableDiffusionPipeline: - pipe = None - - if name == "txt2img": - pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16,cache_dir="D:/huggingface/CACHE/") - pipe.unet.load_attn_procs("./") - pipe.safety_checker = lambda images, **kwargs: (images, [False] * len(images)) - elif name == "sketch2img": - controlnet = ControlNetModel.from_pretrained("Abhi5ingh/model_dresscode", torch_dtype=torch.float16,cache_dir="D:/huggingface/CACHE/") - pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet = controlnet, torch_dtype = torch.float16,cache_dir="D:/huggingface/CACHE/") - pipe.unet.load_attn_procs("./") - pipe.safety_checker = lambda images, **kwargs: (images, [False] * len(images)) - - if pipe is None: - raise Exception(f"Pipeline not Found {name}") - - if enable_cpu_offload: - print("Enabling cpu offloading for the given pipeline") - pipe.enable_model_cpu_offload() - else: - pipe = pipe.to("cuda") - return pipe - -def generate( - prompt, - pipeline_name: PIPELINES, - image = None, - num_inference_steps = 30, - negative_prompt = None, - width = 512, - height = 512, - guidance_scale = 7.5, - controlnet_conditioning_scale = None, - enable_cpu_offload= False): - negative_prompt = negative_prompt if negative_prompt else None - p = st.progress(0) - callback = lambda step,*_: p.progress(step/num_inference_steps) - pipe = get_pipelines(pipeline_name,enable_cpu_offload=enable_cpu_offload) - torch.cuda.empty_cache() - - kwargs = dict( - prompt = prompt, - negative_prompt=negative_prompt, - num_inference_steps=num_inference_steps, - callback=callback, - guidance_scale=guidance_scale, - ) - print("kwargs",kwargs) - - if pipeline_name =="sketch2img" and image: - kwargs.update(image=image,controlnet_conditioning_scale=controlnet_conditioning_scale) - elif pipeline_name == "txt2img": - kwargs.update(width = width, height = height) - else: - raise Exception( - f"Cannot generate image for pipeline {pipeline_name} and {prompt}") - images = pipe(**kwargs).images - image = images[0] - - os.makedirs("outputs", exist_ok=True) - - filename = ( - "outputs/" - + re.sub(r"\s+", "_",prompt)[:30] - + f"_{datetime.datetime.now().timestamp()}" - ) - image.save(f"{filename}.png") - with open(f"{filename}.txt", "w") as f: - f.write(f"Prompt: {prompt}\n\nNegative Prompt:{negative_prompt}") - return image diff --git a/spaces/Abubakari/Sepsis-fastapi-prediction-app/Dockerfile b/spaces/Abubakari/Sepsis-fastapi-prediction-app/Dockerfile deleted file mode 100644 index a64500d2926f582016f23c07409ce09abbe21df8..0000000000000000000000000000000000000000 --- a/spaces/Abubakari/Sepsis-fastapi-prediction-app/Dockerfile +++ /dev/null @@ -1,14 +0,0 @@ -FROM python:3.9 - -WORKDIR /code - -COPY ./requirements.txt /code/requirements.txt - -RUN pip install -r /code/requirements.txt - -COPY . . - -# Expose the port on which the application will run -EXPOSE 7860 - -CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"] diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/r/[id]/message/[messageId]/prompt/$types.d.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/r/[id]/message/[messageId]/prompt/$types.d.ts deleted file mode 100644 index 984e7ed4449e9d93e1823b3ee3e4229eac3e84bd..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/types/src/routes/r/[id]/message/[messageId]/prompt/$types.d.ts +++ /dev/null @@ -1,9 +0,0 @@ -import type * as Kit from '@sveltejs/kit'; - -type Expand
-```
-where `{img_str}` is the actual image data. This format makes displaying them in the UI for free. Do note, that this format is required to be exactly the same, the regex used to find the images is: `)
`.
-
-## LLM input
-To describe the input, let's see it on an example prompt:
-```
-text1Blockman Go Editor Aventura APK: Una plataforma de creación de juegos gratis y divertido
-¿Te gustan los juegos de píxeles? ¿Quieres hacer tus propios juegos y compartirlos con otros? Si es así, entonces usted debe probar Blockman Go Editor Adventure APK, una plataforma de creación de juegos gratis y divertido que le permite crear y jugar juegos de píxeles en su dispositivo Android. En este artículo, le diremos qué es Blockman Go Editor Adventure APK, cómo descargarlo e instalarlo, cómo usarlo y cuáles son los beneficios de usarlo.
-¿Qué es Blockman Go Editor aventura APK?
-Blockman Go Editor Aventura APK es una aplicación que tiene dos funciones principales: un fabricante de juegos y un jugador del juego.
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Una aplicación fabricante de juegos para juegos de píxeles
-Blockman Go Editor Aventura APK es una herramienta de desarrollo que integra Editor de escena, Editor de gatillo, Editor de actor, Editor de interfaz de usuario, Editor de guiones, y otras funciones. Proporciona una plataforma de creación completamente gratuita para los amantes de los juegos de píxeles. Puedes usar varias herramientas y características para crear tus propios juegos, como Bed Wars, Jail Break, Sky Wars, Parkour y más. También puedes personalizar la configuración del juego, como el modo, el mapa, las reglas, etc.
-Una aplicación de jugador de juegos para Blockman Go juegos
-Blockman Go Editor Adventure APK es también una aplicación de jugador de juego que le permite jugar juegos hechos por otros usuarios o usted mismo. Puedes navegar y descargar juegos de la comunidad Blockman Go, o subir tus propios juegos para compartirlos con otros. También puedes unirte a juegos multijugador online con otros jugadores de todo el mundo. Puedes chatear con ellos, hacer amigos o competir con ellos.
-¿Cómo descargar e instalar Blockman Go Editor Aventura APK?
-Blockman Go Editor Aventura APK no está disponible en Google Play Store, por lo que necesita descargarlo de otras fuentes. Estos son los pasos para descargarlo e instalarlo en tu dispositivo:
-Descargar desde APKCombo u otras fuentes
- -Habilitar fuentes desconocidas en su dispositivo
-Antes de instalar el archivo APK, es necesario habilitar fuentes desconocidas en el dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo.
-Instalar el archivo APK y lanzar la aplicación
-Después de descargar el archivo APK, localizarlo en su dispositivo y toque en él para instalarlo. Siga las instrucciones de la pantalla para completar la instalación. Una vez instalada, inicie la aplicación y disfrute creando y jugando juegos de píxeles.
-¿Cómo usar Blockman Go Editor Aventura APK?
-Usando Blockman Go Editor Aventura APK es fácil y divertido. Aquí hay algunos consejos sobre cómo usarlo:
-Crea tus propios juegos con varias herramientas y características
-Para crear tus propios juegos, toca el botón "Crear" en la pantalla principal de la aplicación. Verás varias herramientas y características que puedes usar para crear tus juegos. Por ejemplo, puedes usar el Editor de escenas para diseñar la escena del juego, el Editor de disparadores para configurar la lógica del juego, el Editor de actores para crear los personajes del juego, el Editor de interfaz de usuario para diseñar la interfaz del juego y el Editor de guiones para escribir el código del juego. También puedes usar Asset Store para descargar varios activos para tus juegos, como modelos, texturas, sonidos, etc. Puedes previsualizar tus juegos en cualquier momento y probarlos en tu dispositivo.
- -Jugar juegos hechos por otros usuarios o usted mismo
-Para jugar juegos hechos por otros usuarios o por ti mismo, toca el botón "Jugar" en la pantalla principal de la aplicación. Verás una lista de juegos que puedes descargar y jugar. También puedes buscar juegos por palabras clave o categorías. Para jugar a un juego, toca en él y espera a que se cargue. También puedes calificar y comentar los juegos que juegas.
-Comparte tus juegos con la comunidad Blockman Go
- -¿Cuáles son los beneficios de Blockman Go Editor Aventura APK?
-Blockman Go Editor Aventura APK es una gran aplicación para los amantes de los juegos de píxeles. Aquí están algunos de los beneficios de su uso:
-Gratis y fácil de usar
-Blockman Go Editor Aventura APK es completamente gratis para descargar y usar. Usted no necesita pagar nada para crear o jugar juegos. La aplicación también es fácil de usar, con una interfaz fácil de usar e instrucciones claras. No necesitas ninguna experiencia o conocimiento previo para crear o jugar juegos.
-Creativo y divertido
-Blockman Go Editor Aventura APK es una aplicación creativa y divertida que le permite dar rienda suelta a su imaginación y expresarse. Puedes crear cualquier tipo de juego que quieras, con posibilidades y opciones ilimitadas. También puedes jugar juegos hechos por otros usuarios o por ti mismo, y disfrutar de diferentes géneros y estilos de juegos de píxeles.
-Social e interactivo
-Blockman Go Editor Aventura APK es una aplicación social e interactiva que le permite conectarse con otros amantes de los juegos de píxeles de todo el mundo. Puedes chatear con ellos, hacer amigos o competir con ellos. También puedes unirte a juegos multijugador en línea con ellos y divertirte juntos.
-Conclusión
-Blockman Go Editor Aventura APK es una plataforma de creación de juegos gratis y divertido que le permite crear y jugar juegos de píxeles en su dispositivo Android. Es una aplicación fabricante de juegos para juegos de píxeles, y una aplicación de jugador de juego para juegos de Blockman Go. Es fácil de descargar e instalar, fácil de usar, creativo y divertido, y social e interactivo. Si te gustan los juegos de píxeles, definitivamente deberías probar Blockman Go Editor Aventura APK.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre Blockman Go Editor Aventura APK:
-Q: ¿Es seguro usar Blockman Go Editor Adventure APK?
- -Q: ¿Es Blockman Go Editor Adventure APK compatible con mi dispositivo?
-A: Blockman Go Editor Adventure APK es compatible con la mayoría de los dispositivos Android que ejecutan Android 4.1 o superior. Sin embargo, es posible que algunos dispositivos no admitan algunas características o funciones de la aplicación debido a limitaciones de hardware o configuración del sistema.
-Q: ¿Cómo puedo actualizar Blockman Go editor aventura APK?
-A: Para actualizar Blockman Go Editor Adventure APK, es necesario descargar la última versión de la aplicación de APKCombo u otras fuentes, e instalarlo sobre la versión existente. Alternativamente, puede comprobar si hay actualizaciones dentro de la aplicación pulsando en el botón "Configuración" en la pantalla principal de la aplicación, y luego tocando en el "Buscar actualizaciones" opción.
-Q: ¿Cómo puedo contactar al soporte de Blockman Go?
-A: Para contactar con el soporte de Blockman Go, puede enviar un correo electrónico a support@blockmango.net , o visitar su sitio web oficial en https:/ww.blockmango.net. También puedes seguirlos en sus redes sociales, como Facebook, Twitter, Instagram, YouTube, etc.
-Q: ¿Cómo puedo dar retroalimentación o sugerencias para Blockman Go Editor Adventure APK?
-A: Para dar retroalimentación o sugerencias para Blockman Go Editor Adventure APK, puede utilizar la opción "Feedback" dentro de la aplicación, o enviar un correo electrónico a feedback@blockmango.net. También puede calificar y revisar la aplicación en APKCombo u otras fuentes, y compartir sus opiniones e ideas con otros usuarios.
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Frozen City Mod APK ilimitados diamantes: Cómo descargar e instalar
-Si estás buscando un emocionante y desafiante juego de supervivencia, es posible que quieras echar un vistazo a Frozen City. Este es un juego que pondrá a prueba sus habilidades y estrategia a medida que intenta construir y gestionar su base en un páramo congelado. Sin embargo, si desea disfrutar del juego sin limitaciones o restricciones, es posible que desee probar Frozen City Mod APK Unlimited Diamonds. Esta es una versión modificada del juego original que te da acceso a recursos ilimitados, como diamantes, monedas, gemas y más. En este artículo, le diremos qué es Frozen City, qué es Frozen City Mod APK Unlimited Diamonds, y cómo descargarlo e instalarlo en su dispositivo Android.
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¿Qué es la ciudad congelada?
-Frozen City es un juego desarrollado por Game Insight, una compañía que se especializa en crear juegos móviles inmersivos y atractivos. El juego se desarrolla en un mundo post-apocalíptico donde un virus misterioso ha convertido a la mayoría de la población en zombies. Los sobrevivientes tienen que encontrar refugio y recursos en la ciudad congelada, donde tienen que enfrentar no solo a los no-muertos, sino también a otras facciones hostiles y desastres naturales.
-Un juego de supervivencia ambientado en un mundo post-apocalíptico
-En Frozen City, tienes que sobrevivir en un ambiente duro donde cada decisión importa. Tienes que buscar comida, agua, combustible y otros suministros, así como armas artesanales, herramientas y equipo. También tienes que defender tu base de ataques de zombies y raiders, así como explorar la ciudad en busca de pistas y secretos. Tienes que equilibrar tus necesidades y deseos, así como tu moralidad y humanidad.
-Un juego de construcción de bases con múltiples etapas y tareas
- -Un juego con impresionantes gráficos y efectos de sonido
-Frozen City es un juego que cuenta con impresionantes gráficos y efectos de sonido que crean una atmósfera inmersiva. El juego presenta efectos meteorológicos realistas, como nieve, niebla, lluvia y viento. El juego también tiene animaciones detalladas, sombras, iluminación y texturas que hacen que la ciudad se vea viva. El juego también tiene efectos de sonido realistas, como gemidos de zombis, disparos, explosiones y más. El juego también tiene una banda sonora cautivadora que coincide con el estado de ánimo del juego.
-¿Qué es Frozen City Mod APK ilimitados diamantes?
-Frozen City Mod APK ilimitado de diamantes es una versión modificada del juego original que le da recursos ilimitados, tales como diamantes, monedas, gemas, y más. Estos recursos son esenciales para construir y actualizar su base, así como para desbloquear nuevas características y contenido. Sin embargo, en el juego original, estos recursos son limitados y difíciles de conseguir. Tienes que gastar dinero real o esperar largas horas para conseguirlos.
- -Una versión modificada del juego original que te da recursos ilimitados
-Frozen City Mod APK Unlimited Diamonds es una versión hackeada del juego original que evita el sistema de divisas en el juego. Esto significa que puede obtener diamantes, monedas, gemas y otros recursos ilimitados sin gastar dinero ni esperar largas horas. Puede utilizar estos recursos para construir y actualizar su base más rápido, así como acceder a todas las características y contenido del juego sin restricciones.
-Una manera de disfrutar del juego sin gastar dinero real o esperar largas horas
- -Una manera de desbloquear todas las características y el contenido del juego
-Frozen City Mod APK Unlimited Diamonds es una manera de desbloquear todas las características y el contenido del juego que de otra manera están bloqueados o no disponibles en el juego original. Puede desbloquear nuevas ubicaciones, etapas, misiones, armas, equipos, sobrevivientes y más. También puedes acceder a funciones premium, como estatus VIP, artículos exclusivos y bonos. También puedes disfrutar del juego sin anuncios ni interrupciones.
-Cómo descargar e instalar Frozen City Mod APK ilimitados diamantes?
-Si desea probar Frozen City Mod APK Unlimited Diamonds, es necesario descargar e instalar en su dispositivo Android. Sin embargo, debe seguir algunos pasos y precauciones antes de hacerlo. Estos son los pasos que debe seguir:
-Paso 1: Permitir aplicaciones desconocidas en su dispositivo Android
-Dado que Frozen City Mod APK ilimitado de diamantes no está disponible en el oficial de Google Play Store, es necesario permitir que su dispositivo para instalar aplicaciones de fuentes desconocidas. Para hacer esto, vaya a la configuración del dispositivo, luego la seguridad, luego active la opción que dice "fuentes desconocidas" o "permitir la instalación de aplicaciones de fuentes desconocidas". Esto le permitirá instalar aplicaciones que no son de Google Play Store.
-Paso 2: Instalar una aplicación de administrador de archivos en su dispositivo
-También necesitas instalar una aplicación de administrador de archivos en tu dispositivo que te ayudará a localizar y administrar el archivo APK que descargarás. Una aplicación de administrador de archivos es una aplicación que le permite navegar y organizar los archivos y carpetas en su dispositivo. Puede usar cualquier aplicación de administrador de archivos que prefiera, como ES File Explorer, File Manager o Astro File Manager.
-Paso 3: Descargar el archivo APK de una fuente de buena reputación
- --
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- [APKPure] -
- [APKHome] -
- [ModDroid] -
Una vez que encuentre una fuente confiable, haga clic en el botón de descarga o enlace y espere a que la descarga termine.
-Paso 4: Localizar y tocar el archivo APK para instalarlo
-Después de descargar el archivo APK, necesita localizarlo en su dispositivo usando la aplicación de administrador de archivos que instaló anteriormente. El archivo APK debe estar en la carpeta de descargas o en la carpeta donde lo guardó. Una vez que lo encuentre, pulse sobre él para iniciar el proceso de instalación. Es posible que vea una ventana emergente pidiendo su permiso para instalar la aplicación. Toque en "instalar" o "permitir" y espere a que se complete la instalación.
-Paso 5: Disfruta del juego con diamantes ilimitados y otros recursos
-Felicidades! Usted ha descargado e instalado con éxito Frozen City Mod APK ilimitados diamantes en su dispositivo Android. Ahora puedes disfrutar del juego con recursos ilimitados, como diamantes, monedas, gemas y más. También puede desbloquear todas las características y el contenido del juego sin restricciones o limitaciones. ¡Diviértete jugando Frozen City Mod APK Unlimited Diamonds!
-Conclusión
-Frozen City es un juego de supervivencia que pondrá a prueba tus habilidades y estrategia mientras intentas construir y gestionar tu base en un desierto helado. Sin embargo, si quieres disfrutar del juego sin ningún tipo de molestia o frustración, es posible que desee probar Frozen City Mod APK Unlimited Diamonds. Esta es una versión modificada del juego original que te da recursos ilimitados, como diamantes, monedas, gemas y más. También puedes desbloquear todas las características y contenido del juego sin restricciones o limitaciones.
- -Preguntas frecuentes
-Aquí están algunas de las preguntas más frecuentes sobre Frozen City Mod APK Unlimited Diamonds:
-Q: ¿Es seguro usar Frozen City Mod APK Unlimited Diamonds?
-A: Sí, Frozen City Mod APK Unlimited Diamonds es seguro de usar, siempre y cuando lo descargue de una fuente confiable y siga los pasos y precauciones mencionados en este artículo. Sin embargo, siempre debe tener cuidado al instalar aplicaciones de fuentes desconocidas, ya que pueden contener malware o virus que pueden dañar su dispositivo o robar sus datos. También debe escanear el archivo APK con una aplicación antivirus antes de instalarlo.
-Q: ¿Es Frozen City Mod APK ilimitado diamantes compatible con mi dispositivo?
-A: Frozen City Mod APK Unlimited Diamonds es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego o el mod debido a diferentes especificaciones o configuraciones. Usted debe comprobar la compatibilidad de su dispositivo antes de descargar e instalar el mod.
-Q: ¿Los diamantes ilimitados de Frozen City Mod APK afectarán el rendimiento de mi dispositivo?
-A: Frozen City Mod APK ilimitado diamantes no debe afectar el rendimiento de su dispositivo significativamente, ya que no requiere mucho espacio de almacenamiento o memoria. Sin embargo, algunos dispositivos pueden experimentar retrasos o fallos debido a la baja RAM o CPU. Deberías cerrar otras aplicaciones y borrar la caché antes de jugar para evitar estos problemas.
-Q: Congelado Ciudad Mod APK ilimitado de diamantes trabajar con la última versión del juego?
-A: Frozen City Mod APK Unlimited Diamonds se actualiza regularmente para que coincida con la última versión del juego. Sin embargo, algunas actualizaciones pueden tardar más que otras en ser lanzadas. Deberías comprobar la versión del mod antes de descargarlo e instalarlo para asegurarte de que es compatible con la última versión del juego.
-Q: ¿Puedo jugar Frozen City Mod APK ilimitados diamantes en línea con otros jugadores?
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Cómo descargar Arthdal Chronicles Episodio 16
-¿Eres fan de Arthdal Chronicles, el drama épico coreano que ha cautivado a millones de espectadores en todo el mundo? Si es así, debes estar esperando ansiosamente el episodio 16, el episodio final de la temporada 1. Pero, ¿cómo puedes verlo online sin problemas? En este artículo, te mostraremos cómo descargar Arthdal Chronicles episodio 16 de Netflix, una de las mejores plataformas de streaming que ofrecen este increíble espectáculo. Pero primero, veamos de qué se trata Arthdal Chronicles y por qué deberías verlo.
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¿Qué es Arthdal Chronicles?
-Arthdal Chronicles es un drama histórico de fantasía que cuenta la historia de la antigua ciudad de Arthdal y sus habitantes que luchan por el poder y la supervivencia. El drama cuenta con un reparto lleno de estrellas que incluye a Song Joong-ki, Jang Dong-gun, Kim Ji-won y Kim Ok-bin. El drama se divide en tres partes: Los Hijos de la Profecía, El Cielo Girando de Adentro hacia Afuera, La Tierra Naciente y El Preludio de Todas las Leyendas.
-En los episodios anteriores, hemos sido testigos de cómo Eunseom (Song Joong-ki), un mitad humano mitad neandertal que nació con un destino especial, escapa de Arthdal y se encuentra con otras tribus que lo ayudan a crecer como líder. También hemos visto cómo Tagon (Jang Dong-gun), un carismático guerrero que también es secretamente un neandertal, llega al poder en Arthdal y se enfrenta a varios desafíos de sus enemigos. Mientras tanto, Tanya (Kim Ji-won), un descendiente del clan Wahan que fue secuestrado por soldados Arthdal, se convierte en la alta sacerdotisa de Arthdal y aprende sobre su verdadera identidad. Y Saya ( Kim Ok-bin), un misterioso hermano gemelo de Eunseom que vive en una cámara secreta, conspira para derrocar a Tagon y apoderarse de Arthdal.
- -Por qué deberías ver Arthdal Chronicles Episodio 16
-Hay muchas razones por las que deberías ver online el episodio 16 de Arthdal Chronicles. Estas son algunas de ellas:
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- Disfrutarás de las impresionantes imágenes y cinematografías que dan vida al antiguo mundo de Arthdal. El drama cuenta con un alto valor de producción y un diseño de escenografía realista que te hará sentir como si fueras parte de la historia. -
- Usted se sorprenderá por la excelente actuación y química de los miembros del reparto que retratan sus personajes complejos y diversos con pasión y habilidad. El drama muestra los talentos de algunos de los mejores actores de Corea que ofrecen actuaciones cautivadoras que te harán reír, llorar y animarlos. -
- Estarás inmerso en la historia rica y original que combina la fantasía, la historia y la cultura de una manera única. El drama explora temas como el poder, el amor, la identidad y el destino de una manera creativa y atractiva que te mantendrá enganchado hasta el final. -
- Estarás satisfecho con el final satisfactorio y gratificante que envolverá la historia de una manera significativa y memorable. El drama promete entregar un final que responderá a todas sus preguntas y le dejará con una sensación de cierre y cumplimiento. -
Entonces, si estás buscando un drama que te entretenga, te inspire y te haga pensar, el episodio 16 de Arthdal Chronicles es la elección perfecta para ti.
-Donde Descargar Arthdal Chronicles Episodio 16
- - | Streaming Platform | Calidad de vídeo | Subtítulos | Precio | Descargar Opción | | -- | --- - - - - - - - | Netflix | HD | Varios idiomas | $8.99/mes (Plan básico) | Sí | | Viki | HD | Varios idiomas | $4.99/mes (Plan estándar) | No | | Viu | HD | Múltiples idiomas | $6.49/month (Plan premium) | Sí | | Kocowa | HD | Solo en inglés | $6.99/month (Plan estándar) | No |Como se puede ver en la tabla, Netflix es la mejor plataforma para descargar Arthdal Chronicles episodio 16 en línea. Tiene la más alta calidad de vídeo, la mayoría de las opciones de subtítulos, el precio más bajo, y la opción de descarga que le permite ver Arthdal Chronicles sin conexión. Por lo tanto, recomendamos Netflix como la mejor plataforma para descargar Arthdal Chronicles episodio 16.
- -Cómo descargar Arthdal Chronicles episodio 16 de Netflix
-Si ha decidido descargar Arthdal Chronicles episodio 16 de Netflix, aquí están los pasos que debe seguir:
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- Regístrate en una cuenta de Netflix si aún no tienes una. Puedes elegir entre tres planes: Básico ($8.99/mes), Estándar ($13.99/mes), o Premium ($17.99/mes). El plan básico le permite ver en una pantalla a la vez, el plan estándar le permite ver en dos pantallas a la vez, y el plan Premium le permite ver en cuatro pantallas a la vez. Todos los planes te permiten descargar contenido en tus dispositivos. -
- Descargue la aplicación de Netflix en su dispositivo si no la tiene ya. Puede descargarla desde la App Store o Google Play Store de forma gratuita. -
- Abra la aplicación de Netflix e inicie sesión con los detalles de su cuenta. -
- Buscar "Arthdal Chronicles" en la barra de búsqueda y seleccionarlo de los resultados. -
- Seleccione "Episodio 16" de la lista de episodios y toque en el "Descargar" icono junto a ella. El icono parece una flecha hacia abajo con un círculo alrededor. -
- Espere a que se complete la descarga. Puede comprobar el progreso de la descarga en la sección "Descargas" de la aplicación. - -
Aquí hay una captura de pantalla que muestra cómo descargar Arthdal Chronicles episodio 16 de Netflix:
-
-Un consejo sobre cómo ver Arthdal Chronicles sin conexión: Puede ajustar la calidad de vídeo de sus descargas para ahorrar espacio de almacenamiento en su dispositivo. Para ello, vaya a la sección "Configuración de la aplicación" de la aplicación y toque en "Descargar calidad de vídeo". Puede elegir entre cuatro opciones: Estándar (usa menos espacio de almacenamiento), Alto (usa más espacio de almacenamiento), Medio (usa espacio de almacenamiento moderado) o Inteligente (se ajusta automáticamente a la mejor calidad según las condiciones de su dispositivo y red).
-Conclusión
-En conclusión, Arthdal Chronicles episodio 16 es una visita obligada para todos los fans del drama. Es el último episodio de la temporada 1 que revelará el destino de los personajes y la ciudad de Arthdal. Puede descargar Arthdal Chronicles episodio 16 de Netflix, la mejor plataforma de transmisión que ofrece video de alta calidad, múltiples subtítulos, bajo precio y opción de descarga. Todo lo que necesita hacer es seguir los sencillos pasos que hemos descrito anteriormente y disfrutar viendo Arthdal Chronicles sin conexión. No te pierdas este final épico que te dejará sin palabras.
-Entonces, ¿qué estás esperando? Descarga Arthdal Chronicles episodio 16 de Netflix hoy y presenciar el final de una era.
-Preguntas frecuentes
-Q: ¿Cuándo saldrá la temporada 2 de Arthdal Chronicles?
-A: No hay confirmación oficial todavía sobre si Arthdal Chronicles tendrá una temporada 2 o no. Sin embargo, algunas fuentes sugieren que el equipo de producción está planeando comenzar a filmar la temporada 2 en 2024, después de que los actores terminen su servicio militar. Esperamos que esto sea cierto y que veamos más de Arthdal Chronicles en el futuro.
-Q: ¿Cuántos episodios hay en Arthdal Chronicles?
- -P: ¿Quiénes son los actores principales en Arthdal Chronicles?
-A: Los actores principales de Arthdal Chronicles son Song Joong-ki, Jang Dong-gun, Kim Ji-won y Kim Ok-bin. Song Joong-ki interpreta a Eunseom y Saya, hermanos gemelos que tienen destinos diferentes. Jang Dong-gun interpreta a Tagon, un poderoso guerrero que se convierte en el rey de Arthdal. Kim Ji-won interpreta a Tanya, una alta sacerdotisa que es el interés amoroso de Eunseom. Kim Ok-bin interpreta a Taealha, un político astuto que es el interés amoroso de Tagon.
-P: ¿Cuál es el género de Arthdal Chronicles?
-A: Arthdal Chronicles es un drama histórico de fantasía que combina elementos de mitología, historia y cultura. Se encuentra en una tierra antigua ficticia llamada Arth, donde coexisten diferentes tribus y especies. Explora temas como el poder, el amor, la identidad y el destino.
-Q: ¿Dónde puedo ver Arthdal Chronicles con subtítulos en inglés?
-A: Puedes ver Arthdal Chronicles con subtítulos en inglés en Netflix, Viki, Viu o Kocowa. Sin embargo, recomendamos Netflix como la mejor plataforma para ver Arthdal Chronicles con subtítulos en inglés porque tiene la mejor calidad de video, la mayoría de opciones de subtítulos, el precio más bajo y la opción de descarga.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/pyparsing/helpers.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/pyparsing/helpers.py deleted file mode 100644 index 9588b3b780159a2a2d23c7f84a4404ec350e2b65..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/pyparsing/helpers.py +++ /dev/null @@ -1,1088 +0,0 @@ -# helpers.py -import html.entities -import re -import typing - -from . import __diag__ -from .core import * -from .util import _bslash, _flatten, _escape_regex_range_chars - - -# -# global helpers -# -def delimited_list( - expr: Union[str, ParserElement], - delim: Union[str, ParserElement] = ",", - combine: bool = False, - min: typing.Optional[int] = None, - max: typing.Optional[int] = None, - *, - allow_trailing_delim: bool = False, -) -> ParserElement: - """Helper to define a delimited list of expressions - the delimiter - defaults to ','. By default, the list elements and delimiters can - have intervening whitespace, and comments, but this can be - overridden by passing ``combine=True`` in the constructor. If - ``combine`` is set to ``True``, the matching tokens are - returned as a single token string, with the delimiters included; - otherwise, the matching tokens are returned as a list of tokens, - with the delimiters suppressed. - - If ``allow_trailing_delim`` is set to True, then the list may end with - a delimiter. - - Example:: - - delimited_list(Word(alphas)).parse_string("aa,bb,cc") # -> ['aa', 'bb', 'cc'] - delimited_list(Word(hexnums), delim=':', combine=True).parse_string("AA:BB:CC:DD:EE") # -> ['AA:BB:CC:DD:EE'] - """ - if isinstance(expr, str_type): - expr = ParserElement._literalStringClass(expr) - - dlName = "{expr} [{delim} {expr}]...{end}".format( - expr=str(expr.copy().streamline()), - delim=str(delim), - end=" [{}]".format(str(delim)) if allow_trailing_delim else "", - ) - - if not combine: - delim = Suppress(delim) - - if min is not None: - if min < 1: - raise ValueError("min must be greater than 0") - min -= 1 - if max is not None: - if min is not None and max <= min: - raise ValueError("max must be greater than, or equal to min") - max -= 1 - delimited_list_expr = expr + (delim + expr)[min, max] - - if allow_trailing_delim: - delimited_list_expr += Opt(delim) - - if combine: - return Combine(delimited_list_expr).set_name(dlName) - else: - return delimited_list_expr.set_name(dlName) - - -def counted_array( - expr: ParserElement, - int_expr: typing.Optional[ParserElement] = None, - *, - intExpr: typing.Optional[ParserElement] = None, -) -> ParserElement: - """Helper to define a counted list of expressions. - - This helper defines a pattern of the form:: - - integer expr expr expr... - - where the leading integer tells how many expr expressions follow. - The matched tokens returns the array of expr tokens as a list - the - leading count token is suppressed. - - If ``int_expr`` is specified, it should be a pyparsing expression - that produces an integer value. - - Example:: - - counted_array(Word(alphas)).parse_string('2 ab cd ef') # -> ['ab', 'cd'] - - # in this parser, the leading integer value is given in binary, - # '10' indicating that 2 values are in the array - binary_constant = Word('01').set_parse_action(lambda t: int(t[0], 2)) - counted_array(Word(alphas), int_expr=binary_constant).parse_string('10 ab cd ef') # -> ['ab', 'cd'] - - # if other fields must be parsed after the count but before the - # list items, give the fields results names and they will - # be preserved in the returned ParseResults: - count_with_metadata = integer + Word(alphas)("type") - typed_array = counted_array(Word(alphanums), int_expr=count_with_metadata)("items") - result = typed_array.parse_string("3 bool True True False") - print(result.dump()) - - # prints - # ['True', 'True', 'False'] - # - items: ['True', 'True', 'False'] - # - type: 'bool' - """ - intExpr = intExpr or int_expr - array_expr = Forward() - - def count_field_parse_action(s, l, t): - nonlocal array_expr - n = t[0] - array_expr <<= (expr * n) if n else Empty() - # clear list contents, but keep any named results - del t[:] - - if intExpr is None: - intExpr = Word(nums).set_parse_action(lambda t: int(t[0])) - else: - intExpr = intExpr.copy() - intExpr.set_name("arrayLen") - intExpr.add_parse_action(count_field_parse_action, call_during_try=True) - return (intExpr + array_expr).set_name("(len) " + str(expr) + "...") - - -def match_previous_literal(expr: ParserElement) -> ParserElement: - """Helper to define an expression that is indirectly defined from - the tokens matched in a previous expression, that is, it looks for - a 'repeat' of a previous expression. For example:: - - first = Word(nums) - second = match_previous_literal(first) - match_expr = first + ":" + second - - will match ``"1:1"``, but not ``"1:2"``. Because this - matches a previous literal, will also match the leading - ``"1:1"`` in ``"1:10"``. If this is not desired, use - :class:`match_previous_expr`. Do *not* use with packrat parsing - enabled. - """ - rep = Forward() - - def copy_token_to_repeater(s, l, t): - if t: - if len(t) == 1: - rep << t[0] - else: - # flatten t tokens - tflat = _flatten(t.as_list()) - rep << And(Literal(tt) for tt in tflat) - else: - rep << Empty() - - expr.add_parse_action(copy_token_to_repeater, callDuringTry=True) - rep.set_name("(prev) " + str(expr)) - return rep - - -def match_previous_expr(expr: ParserElement) -> ParserElement: - """Helper to define an expression that is indirectly defined from - the tokens matched in a previous expression, that is, it looks for - a 'repeat' of a previous expression. For example:: - - first = Word(nums) - second = match_previous_expr(first) - match_expr = first + ":" + second - - will match ``"1:1"``, but not ``"1:2"``. Because this - matches by expressions, will *not* match the leading ``"1:1"`` - in ``"1:10"``; the expressions are evaluated first, and then - compared, so ``"1"`` is compared with ``"10"``. Do *not* use - with packrat parsing enabled. - """ - rep = Forward() - e2 = expr.copy() - rep <<= e2 - - def copy_token_to_repeater(s, l, t): - matchTokens = _flatten(t.as_list()) - - def must_match_these_tokens(s, l, t): - theseTokens = _flatten(t.as_list()) - if theseTokens != matchTokens: - raise ParseException( - s, l, "Expected {}, found{}".format(matchTokens, theseTokens) - ) - - rep.set_parse_action(must_match_these_tokens, callDuringTry=True) - - expr.add_parse_action(copy_token_to_repeater, callDuringTry=True) - rep.set_name("(prev) " + str(expr)) - return rep - - -def one_of( - strs: Union[typing.Iterable[str], str], - caseless: bool = False, - use_regex: bool = True, - as_keyword: bool = False, - *, - useRegex: bool = True, - asKeyword: bool = False, -) -> ParserElement: - """Helper to quickly define a set of alternative :class:`Literal` s, - and makes sure to do longest-first testing when there is a conflict, - regardless of the input order, but returns - a :class:`MatchFirst` for best performance. - - Parameters: - - - ``strs`` - a string of space-delimited literals, or a collection of - string literals - - ``caseless`` - treat all literals as caseless - (default= ``False``) - - ``use_regex`` - as an optimization, will - generate a :class:`Regex` object; otherwise, will generate - a :class:`MatchFirst` object (if ``caseless=True`` or ``asKeyword=True``, or if - creating a :class:`Regex` raises an exception) - (default= ``True``) - - ``as_keyword`` - enforce :class:`Keyword`-style matching on the - generated expressions - (default= ``False``) - - ``asKeyword`` and ``useRegex`` are retained for pre-PEP8 compatibility, - but will be removed in a future release - - Example:: - - comp_oper = one_of("< = > <= >= !=") - var = Word(alphas) - number = Word(nums) - term = var | number - comparison_expr = term + comp_oper + term - print(comparison_expr.search_string("B = 12 AA=23 B<=AA AA>12")) - - prints:: - - [['B', '=', '12'], ['AA', '=', '23'], ['B', '<=', 'AA'], ['AA', '>', '12']] - """ - asKeyword = asKeyword or as_keyword - useRegex = useRegex and use_regex - - if ( - isinstance(caseless, str_type) - and __diag__.warn_on_multiple_string_args_to_oneof - ): - warnings.warn( - "More than one string argument passed to one_of, pass" - " choices as a list or space-delimited string", - stacklevel=2, - ) - - if caseless: - isequal = lambda a, b: a.upper() == b.upper() - masks = lambda a, b: b.upper().startswith(a.upper()) - parseElementClass = CaselessKeyword if asKeyword else CaselessLiteral - else: - isequal = lambda a, b: a == b - masks = lambda a, b: b.startswith(a) - parseElementClass = Keyword if asKeyword else Literal - - symbols: List[str] = [] - if isinstance(strs, str_type): - symbols = strs.split() - elif isinstance(strs, Iterable): - symbols = list(strs) - else: - raise TypeError("Invalid argument to one_of, expected string or iterable") - if not symbols: - return NoMatch() - - # reorder given symbols to take care to avoid masking longer choices with shorter ones - # (but only if the given symbols are not just single characters) - if any(len(sym) > 1 for sym in symbols): - i = 0 - while i < len(symbols) - 1: - cur = symbols[i] - for j, other in enumerate(symbols[i + 1 :]): - if isequal(other, cur): - del symbols[i + j + 1] - break - elif masks(cur, other): - del symbols[i + j + 1] - symbols.insert(i, other) - break - else: - i += 1 - - if useRegex: - re_flags: int = re.IGNORECASE if caseless else 0 - - try: - if all(len(sym) == 1 for sym in symbols): - # symbols are just single characters, create range regex pattern - patt = "[{}]".format( - "".join(_escape_regex_range_chars(sym) for sym in symbols) - ) - else: - patt = "|".join(re.escape(sym) for sym in symbols) - - # wrap with \b word break markers if defining as keywords - if asKeyword: - patt = r"\b(?:{})\b".format(patt) - - ret = Regex(patt, flags=re_flags).set_name(" | ".join(symbols)) - - if caseless: - # add parse action to return symbols as specified, not in random - # casing as found in input string - symbol_map = {sym.lower(): sym for sym in symbols} - ret.add_parse_action(lambda s, l, t: symbol_map[t[0].lower()]) - - return ret - - except re.error: - warnings.warn( - "Exception creating Regex for one_of, building MatchFirst", stacklevel=2 - ) - - # last resort, just use MatchFirst - return MatchFirst(parseElementClass(sym) for sym in symbols).set_name( - " | ".join(symbols) - ) - - -def dict_of(key: ParserElement, value: ParserElement) -> ParserElement: - """Helper to easily and clearly define a dictionary by specifying - the respective patterns for the key and value. Takes care of - defining the :class:`Dict`, :class:`ZeroOrMore`, and - :class:`Group` tokens in the proper order. The key pattern - can include delimiting markers or punctuation, as long as they are - suppressed, thereby leaving the significant key text. The value - pattern can include named results, so that the :class:`Dict` results - can include named token fields. - - Example:: - - text = "shape: SQUARE posn: upper left color: light blue texture: burlap" - attr_expr = (label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join)) - print(attr_expr[1, ...].parse_string(text).dump()) - - attr_label = label - attr_value = Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join) - - # similar to Dict, but simpler call format - result = dict_of(attr_label, attr_value).parse_string(text) - print(result.dump()) - print(result['shape']) - print(result.shape) # object attribute access works too - print(result.as_dict()) - - prints:: - - [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']] - - color: 'light blue' - - posn: 'upper left' - - shape: 'SQUARE' - - texture: 'burlap' - SQUARE - SQUARE - {'color': 'light blue', 'shape': 'SQUARE', 'posn': 'upper left', 'texture': 'burlap'} - """ - return Dict(OneOrMore(Group(key + value))) - - -def original_text_for( - expr: ParserElement, as_string: bool = True, *, asString: bool = True -) -> ParserElement: - """Helper to return the original, untokenized text for a given - expression. Useful to restore the parsed fields of an HTML start - tag into the raw tag text itself, or to revert separate tokens with - intervening whitespace back to the original matching input text. By - default, returns astring containing the original parsed text. - - If the optional ``as_string`` argument is passed as - ``False``, then the return value is - a :class:`ParseResults` containing any results names that - were originally matched, and a single token containing the original - matched text from the input string. So if the expression passed to - :class:`original_text_for` contains expressions with defined - results names, you must set ``as_string`` to ``False`` if you - want to preserve those results name values. - - The ``asString`` pre-PEP8 argument is retained for compatibility, - but will be removed in a future release. - - Example:: - - src = "this is test bold text normal text " - for tag in ("b", "i"): - opener, closer = make_html_tags(tag) - patt = original_text_for(opener + SkipTo(closer) + closer) - print(patt.search_string(src)[0]) - - prints:: - - [' bold text '] - ['text'] - """ - asString = asString and as_string - - locMarker = Empty().set_parse_action(lambda s, loc, t: loc) - endlocMarker = locMarker.copy() - endlocMarker.callPreparse = False - matchExpr = locMarker("_original_start") + expr + endlocMarker("_original_end") - if asString: - extractText = lambda s, l, t: s[t._original_start : t._original_end] - else: - - def extractText(s, l, t): - t[:] = [s[t.pop("_original_start") : t.pop("_original_end")]] - - matchExpr.set_parse_action(extractText) - matchExpr.ignoreExprs = expr.ignoreExprs - matchExpr.suppress_warning(Diagnostics.warn_ungrouped_named_tokens_in_collection) - return matchExpr - - -def ungroup(expr: ParserElement) -> ParserElement: - """Helper to undo pyparsing's default grouping of And expressions, - even if all but one are non-empty. - """ - return TokenConverter(expr).add_parse_action(lambda t: t[0]) - - -def locatedExpr(expr: ParserElement) -> ParserElement: - """ - (DEPRECATED - future code should use the Located class) - Helper to decorate a returned token with its starting and ending - locations in the input string. - - This helper adds the following results names: - - - ``locn_start`` - location where matched expression begins - - ``locn_end`` - location where matched expression ends - - ``value`` - the actual parsed results - - Be careful if the input text contains ``
\
-
'
- return report_history_buffer
-
- submit_btn.click(run_agent, inputs=[input_box, agent_type, report_type], outputs=report)\
- .then(store_report, inputs=[report], outputs=report_history)
-
- with gr.Tab("✒️English Polishing"):
- gr.HTML(english_polishing_html)
- polished_result = gr.Markdown(" Polished result will appear here...", elem_classes="output")
- sentences = gr.Textbox(label="# What would you like to polish?", placeholder="Enter your sentence here")
-
- with gr.Row():
- polish_btn = gr.Button("Polish", elem_id="primary-btn")
-
- with gr.Accordion(label="# Polishing History", elem_id="history", open=False):
- polish_history = gr.Markdown()
-
- def store_polished_result(origin, result):
- global polish_history_buffer
- polish_history_buffer += f'Research History {report_history_num}: \ - {report_history_tasks[-1]}
\ -{content}
\
- \
-
'
- return polish_history_buffer
-
- polish_btn.click(english_polishing, inputs=[sentences], outputs=polished_result) \
- .then(store_polished_result, inputs=[sentences, polished_result], outputs=polish_history)
-
- with gr.Tab("📑Literature Review"):
- gr.HTML(literature_review_html)
-
-demo.queue().launch()
\ No newline at end of file
diff --git a/spaces/Cpp4App/Cpp4App/SEM/region_pp_processing.py b/spaces/Cpp4App/Cpp4App/SEM/region_pp_processing.py
deleted file mode 100644
index c330973689a061c9ec515b370370048911da0d15..0000000000000000000000000000000000000000
--- a/spaces/Cpp4App/Cpp4App/SEM/region_pp_processing.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import csv
-import re
-import spacy
-from bs4 import BeautifulSoup
-
-def get_alifornia(text):
- specialArea = ""
- california = 0
- with open(text, encoding='utf-8') as file_obj:
- for line in file_obj:
- specialArea += line
- if "alifornia" in specialArea:
- california = 1
- return specialArea,california
-
-
-import sys
-maxInt = sys.maxsize
-decrement = True
-while decrement:
- decrement = False
- try:
- csv.field_size_limit(maxInt)
- except OverflowError:
- maxInt = int(maxInt/10)
- decrement = True
-
-
-def get_text(path):
- htmlfile = open(path, 'r', encoding='utf-8')
- htmlhandle = htmlfile.read()
-
- soup = BeautifulSoup(htmlhandle, 'html.parser')
-
- stri = str(soup)
- return stri
-
-
-
-
diff --git a/spaces/Cran-May/SEA-Streamlit/app.py b/spaces/Cran-May/SEA-Streamlit/app.py
deleted file mode 100644
index e157ab4f7b1576337d9be74aca9791775006d366..0000000000000000000000000000000000000000
--- a/spaces/Cran-May/SEA-Streamlit/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import streamlit as st
-from gradio_client import Client
-from time import sleep
-from ctransformers import AutoModelForCausalLM
-# Constants
-TITLE = "兮辞·析辞-常明"
-DESCRIPTION = """
-兮辞·析辞-常明 [SLIDE-SEA-7B]的部署，由SSFW NLPark项目支持
-"""
-
-# Initialize client
-
-
-with st.sidebar:
- # system_promptSide = st.text_input("Optional system prompt:")
- temperatureSide = st.slider("情感/Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
- max_new_tokensSide = st.slider("最大tokens生成数", min_value=0.0, max_value=4096.0, value=4096.0, step=64.0)
- # ToppSide = st.slider("Top-p (nucleus sampling)", min_value=0.0, max_value=1.0, value=0.6, step=0.05)
- # RepetitionpenaltySide = st.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05)
-
-# Load the model
-model = AutoModelForCausalLM.from_pretrained("Cran-May/OpenSLIDE", model_file="SLIDE.0.1.gguf", model_type="mistral", gpu_layers=0)
-ins = '''[INST] <{origin}
\ -{result}
\
- -It can be used to restore your **old photos** or improve **AI-generated faces**.
-To use it, simply upload your image.
-If GFPGAN is helpful, please help to ⭐ the Github Repo and recommend it to your friends 😊
-This demo was forked by [vicalloy](https://github.com/vicalloy), add `face blur` param to optimize painting face enhance. -""" -article = r""" - -[](https://github.com/TencentARC/GFPGAN/releases) -[](https://github.com/TencentARC/GFPGAN) -[
ChatGPT 学術最適化
-
-**このプロジェクトが好きだったら、スターをつけてください。もし、より使いやすい学術用のショートカットキーまたはファンクションプラグインを発明した場合は、issueを発行するかpull requestを作成してください。また、このプロジェクト自体によって翻訳されたREADMEは[英語説明書|](docs/README_EN.md)[日本語説明書|](docs/README_JP.md)[ロシア語説明書|](docs/README_RS.md)[フランス語説明書](docs/README_FR.md)もあります。**
-
-> **注意事項**
->
-> 1. **赤色**のラベルが付いているファンクションプラグイン（ボタン）のみファイルを読み込めます。一部のプラグインはプラグインエリアのドロップダウンメニューにあります。新しいプラグインのPRを歓迎いたします！
->
-> 2. このプロジェクトの各ファイルの機能は`self_analysis.md`（自己解析レポート）で詳しく説明されています。バージョンが追加されると、関連するファンクションプラグインをクリックして、GPTを呼び出して自己解析レポートを再生成することができます。一般的な質問は`wiki`にまとめられています。(`https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98`)
-
-
-
-
-機能 | 説明
---- | ---
-ワンクリック整形 | 論文の文法エラーを一括で正確に修正できます。
-ワンクリック日英翻訳 | 日英翻訳には、ワンクリックで対応できます。
-ワンクリックコード説明 | コードの正しい表示と説明が可能です。
-[カスタムショートカットキー](https://www.bilibili.com/video/BV14s4y1E7jN) | カスタムショートカットキーをサポートします。
-[プロキシサーバーの設定](https://www.bilibili.com/video/BV1rc411W7Dr) | プロキシサーバーの設定をサポートします。
-モジュラーデザイン | カスタム高階関数プラグインと[関数プラグイン]、プラグイン[ホット更新]のサポートが可能です。詳細は[こちら](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[自己プログラム解析](https://www.bilibili.com/video/BV1cj411A7VW) | [関数プラグイン][ワンクリック理解](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A)このプロジェクトのソースコード
-[プログラム解析機能](https://www.bilibili.com/video/BV1cj411A7VW) | [関数プラグイン] ワンクリックで別のPython/C/C++/Java/Lua/...プロジェクトツリーを解析できます。
-論文読解 | [関数プラグイン] LaTeX論文の全文をワンクリックで解読し、要約を生成します。
-LaTeX全文翻訳、整形 | [関数プラグイン] ワンクリックでLaTeX論文を翻訳または整形できます。
-注釈生成 | [関数プラグイン] ワンクリックで関数の注釈を大量に生成できます。
-チャット分析レポート生成 | [関数プラグイン] 実行後、まとめレポートを自動生成します。
-[arxivヘルパー](https://www.bilibili.com/video/BV1LM4y1279X) | [関数プラグイン] 入力したarxivの記事URLで要約をワンクリック翻訳+PDFダウンロードができます。
-[PDF論文全文翻訳機能](https://www.bilibili.com/video/BV1KT411x7Wn) | [関数プラグイン] PDF論文タイトルと要約を抽出し、全文を翻訳します（マルチスレッド）。
-[Google Scholar Integratorヘルパー](https://www.bilibili.com/video/BV19L411U7ia) | [関数プラグイン] 任意のGoogle Scholar検索ページURLを指定すると、gptが興味深い記事を選択します。
-数式/画像/テーブル表示 | 数式のTex形式とレンダリング形式を同時に表示できます。数式、コードのハイライトをサポートしています。
-マルチスレッド関数プラグインサポート | ChatGPTをマルチスレッドで呼び出すことができ、大量のテキストやプログラムを簡単に処理できます。
-ダークグラジオ[テーマ](https://github.com/binary-husky/chatgpt_academic/issues/173)の起動 | 「/?__dark-theme=true」というURLをブラウザに追加することで、ダークテーマに切り替えることができます。
-[多数のLLMモデル](https://www.bilibili.com/video/BV1wT411p7yf)をサポート、[API2D](https://api2d.com/)インターフェースをサポート | GPT3.5、GPT4、[清華ChatGLM](https://github.com/THUDM/ChatGLM-6B)による同時サポートは、とても素晴らしいですね！
-huggingface免科学上网[オンライン版](https://huggingface.co/spaces/qingxu98/gpt-academic) | huggingfaceにログイン後、[このスペース](https://huggingface.co/spaces/qingxu98/gpt-academic)をコピーしてください。
-...... | ......
-
-
-
-
-
-- 新しいインターフェース（config.pyのLAYOUTオプションを変更するだけで、「左右レイアウト」と「上下レイアウト」を切り替えることができます）
-
-
-
-
-
-- すべてのボタンは、functional.pyを読み込んで動的に生成されます。カスタム機能を自由に追加して、クリップボードを解放します
-
-
-
-
-
-- 色を修正/修正
-
-
-
-
-
-- 出力に数式が含まれている場合、TeX形式とレンダリング形式の両方が表示され、コピーと読み取りが容易になります
-
-
-
-
-
-- プロジェクトのコードを見るのが面倒？chatgptに整備されたプロジェクトを直接与えましょう
-
-
-
-
-
-- 多数の大規模言語モデルの混合呼び出し(ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
-
-
-
-
-
-多数の大規模言語モデルの混合呼び出し[huggingfaceテスト版](https://huggingface.co/spaces/qingxu98/academic-chatgpt-beta)(huggigface版はchatglmをサポートしていません)
-
-
----
-
-## インストール-方法1：直接運転 (Windows、LinuxまたはMacOS)
-
-1. プロジェクトをダウンロードします。
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. API_KEYとプロキシ設定を構成する
-
-`config.py`で、海外のProxyとOpenAI API KEYを構成して説明します。
-```
-1.あなたが中国にいる場合、OpenAI APIをスムーズに使用するには海外プロキシを設定する必要があります。構成の詳細については、config.py（1.その中のUSE_PROXYをTrueに変更し、2.手順に従ってプロキシを変更する）を詳細に読んでください。
-2. OpenAI API KEYを構成する。OpenAIのウェブサイトでAPI KEYを取得してください。一旦API KEYを手に入れると、config.pyファイルで設定するだけです。
-3.プロキシネットワークに関連する問題(ネットワークタイムアウト、プロキシが動作しない）をhttps://github.com/binary-husky/chatgpt_academic/issues/1にまとめました。
-```
-(P.S. プログラム実行時にconfig.pyの隣にconfig_private.pyという名前のプライバシー設定ファイルを作成し、同じ名前の設定を上書きするconfig_private.pyが存在するかどうかを優先的に確認します。そのため、私たちの構成読み取りロジックを理解できる場合は、config.pyの隣にconfig_private.pyという名前の新しい設定ファイルを作成し、その中のconfig.pyから設定を移動してください。config_private.pyはgitで保守されていないため、プライバシー情報をより安全にすることができます。)
-
-3. 依存関係をインストールします。
-```sh
-# 選択肢があります。
-python -m pip install -r requirements.txt
-
-
-# (選択肢2) もしAnacondaを使用する場合、手順は同様です：
-# (選択肢2.1) conda create -n gptac_venv python=3.11
-# (選択肢2.2) conda activate gptac_venv
-# (選択肢2.3) python -m pip install -r requirements.txt
-
-# 注: 公式のpipソースまたはAlibabaのpipソースを使用してください。 別のpipソース（例：一部の大学のpip）は問題が発生する可能性があります。 一時的なソースの切り替え方法：
-# python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-```
-
-もしあなたが清華ChatGLMをサポートする必要がある場合、さらに多くの依存関係をインストールする必要があります（Pythonに慣れない方やコンピューターの設定が十分でない方は、試みないことをお勧めします）：
-```sh
-python -m pip install -r request_llm/requirements_chatglm.txt
-```
-
-4. 実行
-```sh
-python main.py
-```
-
-5. 関数プラグインのテスト
-```
-- Pythonプロジェクト分析のテスト
- 入力欄に `./crazy_functions/test_project/python/dqn` と入力し、「Pythonプロジェクト全体の解析」をクリックします。
-- 自己コード解読のテスト
- 「[マルチスレッドデモ] このプロジェクト自体を解析します（ソースを翻訳して解読します）」をクリックします。
-- 実験的な機能テンプレート関数のテスト（GPTが「今日の歴史」に何が起こったかを回答することが求められます）。この関数をテンプレートとして使用して、より複雑な機能を実装できます。
- 「[関数プラグインテンプレートデモ] 今日の歴史」をクリックします。
-- 関数プラグインエリアのドロップダウンメニューには他にも選択肢があります。
-```
-
-## インストール方法2：Dockerを使用する（Linux）
-
-1. ChatGPTのみ（大多数の人にお勧めです）
-``` sh
-# プロジェクトのダウンロード
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-# 海外プロキシとOpenAI API KEYの設定
-config.pyを任意のテキストエディタで編集する
-# インストール
-docker build -t gpt-academic .
-# 実行
-docker run --rm -it --net=host gpt-academic
-
-# 関数プラグインのテスト
-## 関数プラグインテンプレート関数のテスト（GPTが「今日の歴史」に何が起こったかを回答することが求められます）。この関数をテンプレートとして使用して、より複雑な機能を実装できます。
-「[関数プラグインテンプレートデモ] 今日の歴史」をクリックします。
-## Latexプロジェクトの要約を書くテスト
-入力欄に./crazy_functions/test_project/latex/attentionと入力し、「テックス論文を読んで要約を書く」をクリックします。
-## Pythonプロジェクト分析のテスト
-入力欄に./crazy_functions/test_project/python/dqnと入力し、[Pythonプロジェクトの全解析]をクリックします。
-
-関数プラグインエリアのドロップダウンメニューには他にも選択肢があります。
-```
-
-2. ChatGPT + ChatGLM（Dockerに非常に詳しい人+十分なコンピューター設定が必要）
-
-
-
-```sh
-# Dockerfileの編集
-cd docs && nano Dockerfile+ChatGLM
-# ビルド方法
-docker build -t gpt-academic --network=host -f Dockerfile+ChatGLM .
-# 実行方法 (1) 直接実行:
-docker run --rm -it --net=host --gpus=all gpt-academic
-# 実行方法 (2) コンテナに入って調整する:
-docker run --rm -it --net=host --gpus=all gpt-academic bash
-```
-
-## インストール方法3：その他のデプロイ方法
-
-1. クラウドサーバーデプロイ
-[デプロイwiki-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-2. WSL2を使用 (Windows Subsystem for Linux)
-[デプロイwiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-
-## インストール-プロキシ設定
-1. 通常の方法
-[プロキシを設定する](https://github.com/binary-husky/chatgpt_academic/issues/1)
-
-2. 初心者向けチュートリアル
-[初心者向けチュートリアル](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BB%A3%E7%90%86%E8%BD%AF%E4%BB%B6%E9%97%AE%E9%A2%98%E7%9A%84%E6%96%B0%E6%89%8B%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95%EF%BC%88%E6%96%B9%E6%B3%95%E5%8F%AA%E9%80%82%E7%94%A8%E4%BA%8E%E6%96%B0%E6%89%8B%EF%BC%89)
-
-
----
-
-## カスタムボタンの追加(学術ショートカットキー)
-
-`core_functional.py`を任意のテキストエディタで開き、以下のエントリーを追加し、プログラムを再起動してください。(ボタンが追加されて表示される場合、前置詞と後置詞はホット編集がサポートされているため、プログラムを再起動せずに即座に有効になります。)
-
-例:
-```
-"超级英译中": {
- # 前置詞 - あなたの要求を説明するために使用されます。翻訳、コードの説明、編集など。
- "Prefix": "以下のコンテンツを中国語に翻訳して、マークダウンテーブルを使用して専門用語を説明してください。\n\n",
-
- # 後置詞 - プレフィックスと共に使用すると、入力内容を引用符で囲むことができます。
- "Suffix": "",
-},
-```
-
-
-
-
-
-
-
----
-
-## いくつかの機能の例
-
-### 画像表示：
-
-
-
-
-
-
-
-### プログラムが自己解析できる場合：
-
-
-
-
-
-
-
-
-
-
-
-### 他のPython/Cppプロジェクトの解析：
-
-
-
-
-
-
-
-
-
-
-
-### Latex論文の一括読解と要約生成
-
-
-
-
-
-
-### 自動報告生成
-
-
-
-
-
-
-
-
-### モジュール化された機能デザイン
-
-
-
-
-
-
-
-
-
-
-### ソースコードの英語翻訳
-
-
-
-
-
-
-
-## Todo およびバージョン計画:
-- version 3.2+ (todo): 関数プラグインがより多くのパラメーターインターフェースをサポートするようになります。
-- version 3.1: 複数のgptモデルを同時にクエリし、api2dをサポートし、複数のapikeyの負荷分散をサポートします。
-- version 3.0: chatglmおよび他の小型llmのサポート
-- version 2.6: プラグイン構造を再構成し、相互作用性を高め、より多くのプラグインを追加しました。
-- version 2.5: 自己更新。総括的な大規模プロジェクトのソースコードをまとめた場合、テキストが長すぎる、トークンがオーバーフローする問題を解決します。
-- version 2.4: (1)PDF全文翻訳機能を追加。(2)入力エリアの位置を切り替える機能を追加。(3)垂直レイアウトオプションを追加。(4)マルチスレッド関数プラグインの最適化。
-- version 2.3: 多スレッドの相互作用性を向上させました。
-- version 2.2: 関数プラグインでホットリロードをサポート
-- version 2.1: 折りたたみ式レイアウト
-- version 2.0: モジュール化された関数プラグインを導入
-- version 1.0: 基本機能
-
-## 参考および学習
-
-
-以下は中国語のマークダウンファイルです。日本語に翻訳してください。既存のマークダウンコマンドを変更しないでください：
-
-```
-多くの優秀なプロジェクトの設計を参考にしています。主なものは以下の通りです：
-
-# 参考プロジェクト1：ChuanhuChatGPTから多くのテクニックを借用
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# 参考プロジェクト2：清華ChatGLM-6B：
-https://github.com/THUDM/ChatGLM-6B
-```
-
diff --git a/spaces/Francesco/FairytaleDJ/data.py b/spaces/Francesco/FairytaleDJ/data.py
deleted file mode 100644
index a6275bfc0054242941b47d67ae3e0e323ee3174e..0000000000000000000000000000000000000000
--- a/spaces/Francesco/FairytaleDJ/data.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from dotenv import load_dotenv
-
-load_dotenv()
-import json
-import os
-
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.llms import OpenAI
-from langchain.vectorstores import DeepLake
-
-from names import DATASET_ID, MODEL_ID
-
-
-def create_db(dataset_path: str, json_filepath: str) -> DeepLake:
- with open(json_filepath, "r") as f:
- data = json.load(f)
-
- texts = []
- metadatas = []
-
- for movie, lyrics in data.items():
- for lyric in lyrics:
- texts.append(lyric["text"])
- metadatas.append(
- {
- "movie": movie,
- "name": lyric["name"],
- "embed_url": lyric["embed_url"],
- }
- )
-
- embeddings = OpenAIEmbeddings(model=MODEL_ID)
-
- db = DeepLake.from_texts(
- texts, embeddings, metadatas=metadatas, dataset_path=dataset_path
- )
-
- return db
-
-
-def load_db(dataset_path: str, *args, **kwargs) -> DeepLake:
- db = DeepLake(dataset_path, *args, **kwargs)
- return db
-
-
-if __name__ == "__main__":
- dataset_path = f"hub://{os.environ['ACTIVELOOP_ORG_ID']}/{DATASET_ID}"
- create_db(dataset_path, "data/emotions_with_spotify_url.json")
diff --git a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/__init__.py b/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/__init__.py
deleted file mode 100644
index 11e5586c347c3071a9d1aca0425d112f45402e85..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/__init__.py
+++ /dev/null
@@ -1,60 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, symbols, cleaner_names):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- cleaner_names: names of the cleaner functions to run the text through
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- sequence = []
- symbol_to_id = {s: i for i, s in enumerate(symbols)}
- clean_text = _clean_text(text, cleaner_names)
- print(clean_text)
- print(f" length:{len(clean_text)}")
- for symbol in clean_text:
- if symbol not in symbol_to_id.keys():
- continue
- symbol_id = symbol_to_id[symbol]
- sequence += [symbol_id]
- print(f" length:{len(sequence)}")
- return sequence
-
-
-def cleaned_text_to_sequence(cleaned_text, symbols):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- symbol_to_id = {s: i for i, s in enumerate(symbols)}
- sequence = [symbol_to_id[symbol] for symbol in cleaned_text if symbol in symbol_to_id.keys()]
- return sequence
-
-
-def sequence_to_text(sequence):
- '''Converts a sequence of IDs back to a string'''
- result = ''
- for symbol_id in sequence:
- s = _id_to_symbol[symbol_id]
- result += s
- return result
-
-
-def _clean_text(text, cleaner_names):
- for name in cleaner_names:
- cleaner = getattr(cleaners, name)
- if not cleaner:
- raise Exception('Unknown cleaner: %s' % name)
- text = cleaner(text)
- return text
diff --git a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/symbols.py b/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/symbols.py
deleted file mode 100644
index 789e9df25d3d93d1976ef22d15d77f51d170ed00..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/text/symbols.py
+++ /dev/null
@@ -1,76 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-# japanese_cleaners
-# _pad = '_'
-# _punctuation = ',.!?-'
-# _letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-
-
-'''# japanese_cleaners2
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-
-'''# korean_cleaners
-_pad = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# # zh_ja_mixture_cleaners
-# _pad = '_'
-# _punctuation = ',.!?-~…'
-# _letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-'''# sanskrit_cleaners
-_pad = '_'
-_punctuation = '।'
-_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
-'''
-
-'''# cjks_cleaners
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
-'''
-
-'''# thai_cleaners
-_pad = '_'
-_punctuation = '.!? '
-_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
-'''
-
-# # cjke_cleaners2
-_pad = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
-
-
-'''# shanghainese_cleaners
-_pad = '_'
-_punctuation = ',.!?…'
-_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
-'''
-
-'''# chinese_dialect_cleaners
-_pad = '_'
-_punctuation = ',.!?~…─'
-_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚ᴀᴇ↑↓∅ⱼ '
-'''
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/attentions.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/attentions.py
deleted file mode 100644
index 9f92f8ead13bc189c0cb5af261f29a9dc5be71df..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/attentions.py
+++ /dev/null
@@ -1,307 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from modules import LayerNorm
-
-
-
-class Encoder(nn.Module):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
- self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert t_s == t_t, "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert t_s == t_t, "Local attention is only available for self-attention."
- block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
- output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
- output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # padd along column
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
- x_flat = x.view([batch, heads, length**2 + length*(length -1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- # self.conv_1 = layers.Conv1d(in_channels, filter_channels, kernel_size, r = 4, lora_alpha = 16, lora_dropout = 0.05)
- # self.conv_2 = layers.Conv1d(filter_channels, out_channels, kernel_size, r = 4, lora_alpha = 16, lora_dropout = 0.05)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/GoldMan/img2prompt/app.py b/spaces/GoldMan/img2prompt/app.py
deleted file mode 100644
index 5bbfee4dd297b0fd3e3e57e8550cb56e039149b2..0000000000000000000000000000000000000000
--- a/spaces/GoldMan/img2prompt/app.py
+++ /dev/null
@@ -1,510 +0,0 @@
-import argparse, os
-import cv2
-import torch
-import cv2
-import PIL
-import gc
-import numpy as np
-import subprocess
-from omegaconf import OmegaConf
-from PIL import Image, ImageDraw
-from tqdm import tqdm, trange
-from imwatermark import WatermarkEncoder
-from einops import rearrange, repeat
-from itertools import islice
-from einops import rearrange
-import time
-from pytorch_lightning import seed_everything
-from torch import autocast
-
-from ldm.util import instantiate_from_config
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.plms import PLMSSampler
-
-def put_watermark(img, wm_encoder=None):
- if wm_encoder is not None:
- img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
- img = wm_encoder.encode(img, 'dwtDct')
- img = Image.fromarray(img[:, :, ::-1])
- return img
-
-def chunk(it, size):
- it = iter(it)
- return iter(lambda: tuple(islice(it, size)), ())
-
-def numpy_to_pil(images):
- """
- Convert a numpy image or a batch of images to a PIL image.
- """
- if images.ndim == 3:
- images = images[None, ...]
- images = (images * 255).round().astype("uint8")
- pil_images = [Image.fromarray(image) for image in images]
-
- return pil_images
-
-def load_model_from_config(config, ckpt, verbose=False):
- print(f"Loading model from {ckpt}")
- pl_sd = torch.load(ckpt, map_location="cpu")
- if "global_step" in pl_sd:
- print(f"Global Step: {pl_sd['global_step']}")
- sd = pl_sd["state_dict"]
- model = instantiate_from_config(config.model)
- m, u = model.load_state_dict(sd, strict=False)
- if len(m) > 0 and verbose:
- print("missing keys:")
- print(m)
- if len(u) > 0 and verbose:
- print("unexpected keys:")
- print(u)
-
- model.cuda()
- model.eval()
- return model
-
-def load_img(path):
- image = Image.open(path).convert("RGB")
- w, h = image.size
- print(f"loaded input image of size ({w}, {h}) from {path}")
- w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
- image = image.resize((w, h), resample=PIL.Image.Resampling.LANCZOS)
- image = np.array(image).astype(np.float32) / 255.0
- image = image[None].transpose(0, 3, 1, 2)
- image = torch.from_numpy(image)
- return 2.*image - 1.
-
-def convert_pil_img(image):
- w, h = image.size
- w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
- image = image.resize((w, h), resample=PIL.Image.Resampling.LANCZOS)
- image = np.array(image).astype(np.float32) / 255.0
- image = image[None].transpose(0, 3, 1, 2)
- image = torch.from_numpy(image)
- return 2.*image - 1.
-
-def addalpha(im, mask):
- imr, img, imb, ima = im.split()
- mmr, mmg, mmb, mma = mask.split()
- im = Image.merge('RGBA', [imr, img, imb, mma]) # we want the RGB from the original, but the transparency from the mask
- return(im)
-
-# Alternative method composites a grid of images at the positions provided
-def grid_merge(source, slices):
- source.convert("RGBA")
- for slice, posx, posy in slices: # go in reverse to get proper stacking
- source.alpha_composite(slice, (posx, posy))
- return source
-
-def grid_coords(target, original, overlap):
- #generate a list of coordinate tuples for our sections, in order of how they'll be rendered
- #target should be the size for the gobig result, original is the size of each chunk being rendered
- center = []
- target_x, target_y = target
- center_x = int(target_x / 2)
- center_y = int(target_y / 2)
- original_x, original_y = original
- x = center_x - int(original_x / 2)
- y = center_y - int(original_y / 2)
- center.append((x,y)) #center chunk
- uy = y #up
- uy_list = []
- dy = y #down
- dy_list = []
- lx = x #left
- lx_list = []
- rx = x #right
- rx_list = []
- while uy > 0: #center row vertical up
- uy = uy - original_y + overlap
- uy_list.append((lx, uy))
- while (dy + original_y) <= target_y: #center row vertical down
- dy = dy + original_y - overlap
- dy_list.append((rx, dy))
- while lx > 0:
- lx = lx - original_x + overlap
- lx_list.append((lx, y))
- uy = y
- while uy > 0:
- uy = uy - original_y + overlap
- uy_list.append((lx, uy))
- dy = y
- while (dy + original_y) <= target_y:
- dy = dy + original_y - overlap
- dy_list.append((lx, dy))
- while (rx + original_x) <= target_x:
- rx = rx + original_x - overlap
- rx_list.append((rx, y))
- uy = y
- while uy > 0:
- uy = uy - original_y + overlap
- uy_list.append((rx, uy))
- dy = y
- while (dy + original_y) <= target_y:
- dy = dy + original_y - overlap
- dy_list.append((rx, dy))
- # calculate a new size that will fill the canvas, which will be optionally used in grid_slice and go_big
- last_coordx, last_coordy = dy_list[-1:][0]
- render_edgey = last_coordy + original_y # outer bottom edge of the render canvas
- render_edgex = last_coordx + original_x # outer side edge of the render canvas
- scalarx = render_edgex / target_x
- scalary = render_edgey / target_y
- if scalarx <= scalary:
- new_edgex = int(target_x * scalarx)
- new_edgey = int(target_y * scalarx)
- else:
- new_edgex = int(target_x * scalary)
- new_edgey = int(target_y * scalary)
- # now put all the chunks into one master list of coordinates (essentially reverse of how we calculated them so that the central slices will be on top)
- result = []
- for coords in dy_list[::-1]:
- result.append(coords)
- for coords in uy_list[::-1]:
- result.append(coords)
- for coords in rx_list[::-1]:
- result.append(coords)
- for coords in lx_list[::-1]:
- result.append(coords)
- result.append(center[0])
- return result, (new_edgex, new_edgey)
-
-def get_resampling_mode():
- try:
- from PIL import __version__, Image
- major_ver = int(__version__.split('.')[0])
- if major_ver >= 9:
- return Image.Resampling.LANCZOS
- else:
- return Image.LANCZOS
- except Exception as ex:
- return 1 # 'Lanczos' irrespective of version.
-
-# Chop our source into a grid of images that each equal the size of the original render
-def grid_slice(source, overlap, og_size, maximize=False):
- width, height = og_size # size of the slices to be rendered
- coordinates, new_size = grid_coords(source.size, og_size, overlap)
- if maximize == True:
- source = source.resize(new_size, get_resampling_mode()) # minor concern that we're resizing twice
- coordinates, new_size = grid_coords(source.size, og_size, overlap) # re-do the coordinates with the new canvas size
- # loc_width and loc_height are the center point of the goal size, and we'll start there and work our way out
- slices = []
- for coordinate in coordinates:
- x, y = coordinate
- slices.append(((source.crop((x, y, x+width, y+height))), x, y))
- global slices_todo
- slices_todo = len(slices) - 1
- return slices, new_size
-
-class Options:
- prompt: str
- outdir: str
- ddim_steps: int
- n_iter: int
- H: int
- W: int
- C: int
- f: int
- scale: float
- strength: float
- from_file: bool
- config: str
- ckpt: str
- passes: int
- wm: str
- realesrgan: str
-
-def main():
- parser = argparse.ArgumentParser()
-
- parser.add_argument(
- "--prompt",
- type=str,
- nargs="?",
- help="the prompt to render"
- )
- parser.add_argument(
- "--outdir",
- type=str,
- nargs="?",
- help="dir to write results to",
- default="outputs/txt2imghd-samples"
- )
- parser.add_argument(
- "--steps",
- type=int,
- default=150,
- help="number of sampling steps",
- )
- parser.add_argument(
- "--ddim",
- action='store_true',
- help="use ddim sampling",
- )
- parser.add_argument(
- "--n_iter",
- type=int,
- default=1,
- help="sample this often",
- )
- parser.add_argument(
- "--H",
- type=int,
- default=512,
- help="image height, in pixel space",
- )
- parser.add_argument(
- "--W",
- type=int,
- default=512,
- help="image width, in pixel space",
- )
- parser.add_argument(
- "--C",
- type=int,
- default=4,
- help="latent channels",
- )
- parser.add_argument(
- "--f",
- type=int,
- default=8,
- help="downsampling factor",
- )
- parser.add_argument(
- "--scale",
- type=float,
- default=10,
- help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
- )
- parser.add_argument(
- "--from-file",
- type=str,
- help="if specified, load prompts from this file",
- )
- parser.add_argument(
- "--seed",
- type=int,
- default=42,
- help="the seed (for reproducible sampling)",
- )
- parser.add_argument(
- "--config",
- type=str,
- default="configs/stable-diffusion/v1-inference.yaml",
- help="path to config which constructs model",
- )
- parser.add_argument(
- "--ckpt",
- type=str,
- default="models/ldm/stable-diffusion-v1/model.ckpt",
- help="path to checkpoint of model",
- )
- parser.add_argument(
- "--strength",
- type=float,
- default=0.3,
- help="strength for noising/unnoising. 1.0 corresponds to full destruction of information in init image",
- )
- parser.add_argument(
- "--passes",
- type=int,
- default=1,
- help="number of upscales/details",
- )
- parser.add_argument(
- "--wm",
- type=str,
- default="txt2imghd",
- help="watermark text",
- )
- parser.add_argument(
- "--realesrgan",
- type=str,
- default="realesrgan-ncnn-vulkan",
- help="path to realesrgan executable"
- )
- opt = parser.parse_args()
-
- if opt.prompt is None:
- opt.prompt = input("prompt: ")
- text2img2(opt)
-
-def realesrgan2x(executable: str, input: str, output: str):
- process = subprocess.Popen([
- executable,
- '-i',
- input,
- '-o',
- output,
- '-n',
- 'realesrgan-x4plus'
- ])
- process.wait()
-
- final_output = Image.open(output)
- final_output = final_output.resize((int(final_output.size[0] / 2), int(final_output.size[1] / 2)), get_resampling_mode())
- final_output.save(output)
-
-def text2img2(opt: Options):
-
- seed_everything(opt.seed)
-
- config = OmegaConf.load(f"{opt.config}")
- model = load_model_from_config(config, f"{opt.ckpt}")
-
- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
- model = model.to(device)
-
- if opt.ddim:
- sampler = DDIMSampler(model)
- else:
- sampler = PLMSSampler(model)
-
- os.makedirs(opt.outdir, exist_ok=True)
- outpath = opt.outdir
-
- wm_encoder = WatermarkEncoder()
- wm_encoder.set_watermark('bytes', opt.wm.encode('utf-8'))
-
- batch_size = 1
- if not opt.from_file:
- prompt = opt.prompt
- assert prompt is not None
- data = [batch_size * [prompt]]
-
- else:
- print(f"reading prompts from {opt.from_file}")
- with open(opt.from_file, "r") as f:
- data = f.read().splitlines()
- data = list(chunk(data, batch_size))
-
- sample_path = os.path.join(outpath, "samples")
- os.makedirs(sample_path, exist_ok=True)
- base_count = len(os.listdir(sample_path))
-
- precision_scope = autocast
- generated = []
- with torch.no_grad():
- with precision_scope("cuda"):
- with model.ema_scope():
- tic = time.time()
- all_samples = list()
- for n in trange(opt.n_iter, desc="Sampling"):
- for prompts in tqdm(data, desc="data"):
- uc = None
- if opt.scale != 1.0:
- uc = model.get_learned_conditioning(batch_size * [""])
- if isinstance(prompts, tuple):
- prompts = list(prompts)
- c = model.get_learned_conditioning(prompts)
- shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
- samples_ddim, _ = sampler.sample(S=opt.steps,
- conditioning=c,
- batch_size=batch_size,
- shape=shape,
- verbose=False,
- unconditional_guidance_scale=opt.scale,
- unconditional_conditioning=uc,
- eta=0,
- x_T=None)
-
- x_samples_ddim = model.decode_first_stage(samples_ddim)
- x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
- x_samples_ddim = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
-
- x_checked_image = x_samples_ddim
-
- x_checked_image_torch = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
-
- for x_sample in x_checked_image_torch:
- x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
- img = Image.fromarray(x_sample.astype(np.uint8))
- output_path = os.path.join(sample_path, f"{base_count:05}.png")
- img.save(output_path)
- generated.append(base_count)
- base_count += 1
-
- torch.cuda.empty_cache()
- gc.collect()
-
- sampler = DDIMSampler(model)
-
- gobig_overlap = 64
-
- for init_img_number in generated:
- base_filename = f"{init_img_number:05}"
-
- for _ in trange(opt.passes, desc="Passes"):
- realesrgan2x(opt.realesrgan, os.path.join(sample_path, f"{base_filename}.png"), os.path.join(sample_path, f"{base_filename}u.png"))
- base_filename = f"{base_filename}u"
-
- source_image = Image.open(os.path.join(sample_path, f"{base_filename}.png"))
- og_size = (int(source_image.size[0] / 2), int(source_image.size[1] / 2))
- slices, _ = grid_slice(source_image, gobig_overlap, og_size, False)
-
- betterslices = []
- for _, chunk_w_coords in tqdm(enumerate(slices), "Slices"):
- chunk, coord_x, coord_y = chunk_w_coords
- init_image = convert_pil_img(chunk).to(device)
- init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
- init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space
-
- sampler.make_schedule(ddim_num_steps=opt.steps, ddim_eta=0, verbose=False)
-
- assert 0. <= opt.strength <= 1., 'can only work with strength in [0.0, 1.0]'
- t_enc = int(opt.strength * opt.steps)
-
- with torch.no_grad():
- with precision_scope("cuda"):
- with model.ema_scope():
- for prompts in tqdm(data, desc="data"):
- uc = None
- if opt.scale != 1.0:
- uc = model.get_learned_conditioning(batch_size * [""])
- if isinstance(prompts, tuple):
- prompts = list(prompts)
- c = model.get_learned_conditioning(prompts)
-
- # encode (scaled latent)
- z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))
- # decode it
- samples = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt.scale,
- unconditional_conditioning=uc,)
-
- x_samples = model.decode_first_stage(samples)
- x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
-
- for x_sample in x_samples:
- x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
- resultslice = Image.fromarray(x_sample.astype(np.uint8)).convert('RGBA')
- betterslices.append((resultslice.copy(), coord_x, coord_y))
-
- alpha = Image.new('L', og_size, color=0xFF)
- alpha_gradient = ImageDraw.Draw(alpha)
- a = 0
- i = 0
- overlap = gobig_overlap
- shape = (og_size, (0,0))
- while i < overlap:
- alpha_gradient.rectangle(shape, fill = a)
- a += 4
- i += 1
- shape = ((og_size[0] - i, og_size[1]- i), (i,i))
- mask = Image.new('RGBA', og_size, color=0)
- mask.putalpha(alpha)
- finished_slices = []
- for betterslice, x, y in betterslices:
- finished_slice = addalpha(betterslice, mask)
- finished_slices.append((finished_slice, x, y))
- # # Once we have all our images, use grid_merge back onto the source, then save
- final_output = grid_merge(source_image.convert("RGBA"), finished_slices).convert("RGB")
- final_output.save(os.path.join(sample_path, f"{base_filename}d.png"))
- base_filename = f"{base_filename}d"
-
- torch.cuda.empty_cache()
- gc.collect()
-
- put_watermark(final_output, wm_encoder)
- final_output.save(os.path.join(sample_path, f"{base_filename}.png"))
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/_base_/models/mask_rcnn_swin_fpn.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/_base_/models/mask_rcnn_swin_fpn.py
deleted file mode 100644
index e3d42197f4646cd9ecafac2095d3f8e079f0a729..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/_base_/models/mask_rcnn_swin_fpn.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# model settings
-model = dict(
- type='MaskRCNN',
- pretrained=None,
- backbone=dict(
- type='SwinTransformer',
- embed_dim=96,
- depths=[2, 2, 6, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- mlp_ratio=4.,
- qkv_bias=True,
- qk_scale=None,
- drop_rate=0.,
- attn_drop_rate=0.,
- drop_path_rate=0.2,
- ape=False,
- patch_norm=True,
- out_indices=(0, 1, 2, 3),
- use_checkpoint=False),
- neck=dict(
- type='FPN',
- in_channels=[96, 192, 384, 768],
- out_channels=256,
- num_outs=5),
- rpn_head=dict(
- type='RPNHead',
- in_channels=256,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8],
- ratios=[0.5, 1.0, 2.0],
- strides=[4, 8, 16, 32, 64]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[1.0, 1.0, 1.0, 1.0]),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
- roi_head=dict(
- type='StandardRoIHead',
- bbox_roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32]),
- bbox_head=dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- reg_class_agnostic=False,
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
- mask_roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32]),
- mask_head=dict(
- type='FCNMaskHead',
- num_convs=4,
- in_channels=256,
- conv_out_channels=256,
- num_classes=80,
- loss_mask=dict(
- type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
- # model training and testing settings
- train_cfg=dict(
- rpn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.3,
- min_pos_iou=0.3,
- match_low_quality=True,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=256,
- pos_fraction=0.5,
- neg_pos_ub=-1,
- add_gt_as_proposals=False),
- allowed_border=-1,
- pos_weight=-1,
- debug=False),
- rpn_proposal=dict(
- nms_pre=2000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- min_pos_iou=0.5,
- match_low_quality=True,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=512,
- pos_fraction=0.25,
- neg_pos_ub=-1,
- add_gt_as_proposals=True),
- mask_size=28,
- pos_weight=-1,
- debug=False)),
- test_cfg=dict(
- rpn=dict(
- nms_pre=1000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.5),
- max_per_img=100,
- mask_thr_binary=0.5)))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py
deleted file mode 100644
index 1b48a2104baf0df935954897ae4a991b38684d78..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
-# learning policy
-lr_config = dict(step=[28, 34])
-runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/backbones/resnet.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/backbones/resnet.py
deleted file mode 100644
index 3826815a6d94fdc4c54001d4c186d10ca3380e80..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/backbones/resnet.py
+++ /dev/null
@@ -1,663 +0,0 @@
-import torch.nn as nn
-import torch.utils.checkpoint as cp
-from mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer,
- constant_init, kaiming_init)
-from mmcv.runner import load_checkpoint
-from torch.nn.modules.batchnorm import _BatchNorm
-
-from mmdet.utils import get_root_logger
-from ..builder import BACKBONES
-from ..utils import ResLayer
-
-
-class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self,
- inplanes,
- planes,
- stride=1,
- dilation=1,
- downsample=None,
- style='pytorch',
- with_cp=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- dcn=None,
- plugins=None):
- super(BasicBlock, self).__init__()
- assert dcn is None, 'Not implemented yet.'
- assert plugins is None, 'Not implemented yet.'
-
- self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
- self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
-
- self.conv1 = build_conv_layer(
- conv_cfg,
- inplanes,
- planes,
- 3,
- stride=stride,
- padding=dilation,
- dilation=dilation,
- bias=False)
- self.add_module(self.norm1_name, norm1)
- self.conv2 = build_conv_layer(
- conv_cfg, planes, planes, 3, padding=1, bias=False)
- self.add_module(self.norm2_name, norm2)
-
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
- self.dilation = dilation
- self.with_cp = with_cp
-
- @property
- def norm1(self):
- """nn.Module: normalization layer after the first convolution layer"""
- return getattr(self, self.norm1_name)
-
- @property
- def norm2(self):
- """nn.Module: normalization layer after the second convolution layer"""
- return getattr(self, self.norm2_name)
-
- def forward(self, x):
- """Forward function."""
-
- def _inner_forward(x):
- identity = x
-
- out = self.conv1(x)
- out = self.norm1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.norm2(out)
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
-
- return out
-
- if self.with_cp and x.requires_grad:
- out = cp.checkpoint(_inner_forward, x)
- else:
- out = _inner_forward(x)
-
- out = self.relu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self,
- inplanes,
- planes,
- stride=1,
- dilation=1,
- downsample=None,
- style='pytorch',
- with_cp=False,
- conv_cfg=None,
- norm_cfg=dict(type='BN'),
- dcn=None,
- plugins=None):
- """Bottleneck block for ResNet.
-
- If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
- it is "caffe", the stride-two layer is the first 1x1 conv layer.
- """
- super(Bottleneck, self).__init__()
- assert style in ['pytorch', 'caffe']
- assert dcn is None or isinstance(dcn, dict)
- assert plugins is None or isinstance(plugins, list)
- if plugins is not None:
- allowed_position = ['after_conv1', 'after_conv2', 'after_conv3']
- assert all(p['position'] in allowed_position for p in plugins)
-
- self.inplanes = inplanes
- self.planes = planes
- self.stride = stride
- self.dilation = dilation
- self.style = style
- self.with_cp = with_cp
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- self.dcn = dcn
- self.with_dcn = dcn is not None
- self.plugins = plugins
- self.with_plugins = plugins is not None
-
- if self.with_plugins:
- # collect plugins for conv1/conv2/conv3
- self.after_conv1_plugins = [
- plugin['cfg'] for plugin in plugins
- if plugin['position'] == 'after_conv1'
- ]
- self.after_conv2_plugins = [
- plugin['cfg'] for plugin in plugins
- if plugin['position'] == 'after_conv2'
- ]
- self.after_conv3_plugins = [
- plugin['cfg'] for plugin in plugins
- if plugin['position'] == 'after_conv3'
- ]
-
- if self.style == 'pytorch':
- self.conv1_stride = 1
- self.conv2_stride = stride
- else:
- self.conv1_stride = stride
- self.conv2_stride = 1
-
- self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
- self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
- self.norm3_name, norm3 = build_norm_layer(
- norm_cfg, planes * self.expansion, postfix=3)
-
- self.conv1 = build_conv_layer(
- conv_cfg,
- inplanes,
- planes,
- kernel_size=1,
- stride=self.conv1_stride,
- bias=False)
- self.add_module(self.norm1_name, norm1)
- fallback_on_stride = False
- if self.with_dcn:
- fallback_on_stride = dcn.pop('fallback_on_stride', False)
- if not self.with_dcn or fallback_on_stride:
- self.conv2 = build_conv_layer(
- conv_cfg,
- planes,
- planes,
- kernel_size=3,
- stride=self.conv2_stride,
- padding=dilation,
- dilation=dilation,
- bias=False)
- else:
- assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
- self.conv2 = build_conv_layer(
- dcn,
- planes,
- planes,
- kernel_size=3,
- stride=self.conv2_stride,
- padding=dilation,
- dilation=dilation,
- bias=False)
-
- self.add_module(self.norm2_name, norm2)
- self.conv3 = build_conv_layer(
- conv_cfg,
- planes,
- planes * self.expansion,
- kernel_size=1,
- bias=False)
- self.add_module(self.norm3_name, norm3)
-
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
-
- if self.with_plugins:
- self.after_conv1_plugin_names = self.make_block_plugins(
- planes, self.after_conv1_plugins)
- self.after_conv2_plugin_names = self.make_block_plugins(
- planes, self.after_conv2_plugins)
- self.after_conv3_plugin_names = self.make_block_plugins(
- planes * self.expansion, self.after_conv3_plugins)
-
- def make_block_plugins(self, in_channels, plugins):
- """make plugins for block.
-
- Args:
- in_channels (int): Input channels of plugin.
- plugins (list[dict]): List of plugins cfg to build.
-
- Returns:
- list[str]: List of the names of plugin.
- """
- assert isinstance(plugins, list)
- plugin_names = []
- for plugin in plugins:
- plugin = plugin.copy()
- name, layer = build_plugin_layer(
- plugin,
- in_channels=in_channels,
- postfix=plugin.pop('postfix', ''))
- assert not hasattr(self, name), f'duplicate plugin {name}'
- self.add_module(name, layer)
- plugin_names.append(name)
- return plugin_names
-
- def forward_plugin(self, x, plugin_names):
- out = x
- for name in plugin_names:
- out = getattr(self, name)(x)
- return out
-
- @property
- def norm1(self):
- """nn.Module: normalization layer after the first convolution layer"""
- return getattr(self, self.norm1_name)
-
- @property
- def norm2(self):
- """nn.Module: normalization layer after the second convolution layer"""
- return getattr(self, self.norm2_name)
-
- @property
- def norm3(self):
- """nn.Module: normalization layer after the third convolution layer"""
- return getattr(self, self.norm3_name)
-
- def forward(self, x):
- """Forward function."""
-
- def _inner_forward(x):
- identity = x
- out = self.conv1(x)
- out = self.norm1(out)
- out = self.relu(out)
-
- if self.with_plugins:
- out = self.forward_plugin(out, self.after_conv1_plugin_names)
-
- out = self.conv2(out)
- out = self.norm2(out)
- out = self.relu(out)
-
- if self.with_plugins:
- out = self.forward_plugin(out, self.after_conv2_plugin_names)
-
- out = self.conv3(out)
- out = self.norm3(out)
-
- if self.with_plugins:
- out = self.forward_plugin(out, self.after_conv3_plugin_names)
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
-
- return out
-
- if self.with_cp and x.requires_grad:
- out = cp.checkpoint(_inner_forward, x)
- else:
- out = _inner_forward(x)
-
- out = self.relu(out)
-
- return out
-
-
-@BACKBONES.register_module()
-class ResNet(nn.Module):
- """ResNet backbone.
-
- Args:
- depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
- stem_channels (int | None): Number of stem channels. If not specified,
- it will be the same as `base_channels`. Default: None.
- base_channels (int): Number of base channels of res layer. Default: 64.
- in_channels (int): Number of input image channels. Default: 3.
- num_stages (int): Resnet stages. Default: 4.
- strides (Sequence[int]): Strides of the first block of each stage.
- dilations (Sequence[int]): Dilation of each stage.
- out_indices (Sequence[int]): Output from which stages.
- style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
- layer is the 3x3 conv layer, otherwise the stride-two layer is
- the first 1x1 conv layer.
- deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
- avg_down (bool): Use AvgPool instead of stride conv when
- downsampling in the bottleneck.
- frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
- -1 means not freezing any parameters.
- norm_cfg (dict): Dictionary to construct and config norm layer.
- norm_eval (bool): Whether to set norm layers to eval mode, namely,
- freeze running stats (mean and var). Note: Effect on Batch Norm
- and its variants only.
- plugins (list[dict]): List of plugins for stages, each dict contains:
-
- - cfg (dict, required): Cfg dict to build plugin.
- - position (str, required): Position inside block to insert
- plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'.
- - stages (tuple[bool], optional): Stages to apply plugin, length
- should be same as 'num_stages'.
- with_cp (bool): Use checkpoint or not. Using checkpoint will save some
- memory while slowing down the training speed.
- zero_init_residual (bool): Whether to use zero init for last norm layer
- in resblocks to let them behave as identity.
-
- Example:
- >>> from mmdet.models import ResNet
- >>> import torch
- >>> self = ResNet(depth=18)
- >>> self.eval()
- >>> inputs = torch.rand(1, 3, 32, 32)
- >>> level_outputs = self.forward(inputs)
- >>> for level_out in level_outputs:
- ... print(tuple(level_out.shape))
- (1, 64, 8, 8)
- (1, 128, 4, 4)
- (1, 256, 2, 2)
- (1, 512, 1, 1)
- """
-
- arch_settings = {
- 18: (BasicBlock, (2, 2, 2, 2)),
- 34: (BasicBlock, (3, 4, 6, 3)),
- 50: (Bottleneck, (3, 4, 6, 3)),
- 101: (Bottleneck, (3, 4, 23, 3)),
- 152: (Bottleneck, (3, 8, 36, 3))
- }
-
- def __init__(self,
- depth,
- in_channels=3,
- stem_channels=None,
- base_channels=64,
- num_stages=4,
- strides=(1, 2, 2, 2),
- dilations=(1, 1, 1, 1),
- out_indices=(0, 1, 2, 3),
- style='pytorch',
- deep_stem=False,
- avg_down=False,
- frozen_stages=-1,
- conv_cfg=None,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- dcn=None,
- stage_with_dcn=(False, False, False, False),
- plugins=None,
- with_cp=False,
- zero_init_residual=True):
- super(ResNet, self).__init__()
- if depth not in self.arch_settings:
- raise KeyError(f'invalid depth {depth} for resnet')
- self.depth = depth
- if stem_channels is None:
- stem_channels = base_channels
- self.stem_channels = stem_channels
- self.base_channels = base_channels
- self.num_stages = num_stages
- assert num_stages >= 1 and num_stages <= 4
- self.strides = strides
- self.dilations = dilations
- assert len(strides) == len(dilations) == num_stages
- self.out_indices = out_indices
- assert max(out_indices) < num_stages
- self.style = style
- self.deep_stem = deep_stem
- self.avg_down = avg_down
- self.frozen_stages = frozen_stages
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- self.with_cp = with_cp
- self.norm_eval = norm_eval
- self.dcn = dcn
- self.stage_with_dcn = stage_with_dcn
- if dcn is not None:
- assert len(stage_with_dcn) == num_stages
- self.plugins = plugins
- self.zero_init_residual = zero_init_residual
- self.block, stage_blocks = self.arch_settings[depth]
- self.stage_blocks = stage_blocks[:num_stages]
- self.inplanes = stem_channels
-
- self._make_stem_layer(in_channels, stem_channels)
-
- self.res_layers = []
- for i, num_blocks in enumerate(self.stage_blocks):
- stride = strides[i]
- dilation = dilations[i]
- dcn = self.dcn if self.stage_with_dcn[i] else None
- if plugins is not None:
- stage_plugins = self.make_stage_plugins(plugins, i)
- else:
- stage_plugins = None
- planes = base_channels * 2**i
- res_layer = self.make_res_layer(
- block=self.block,
- inplanes=self.inplanes,
- planes=planes,
- num_blocks=num_blocks,
- stride=stride,
- dilation=dilation,
- style=self.style,
- avg_down=self.avg_down,
- with_cp=with_cp,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- dcn=dcn,
- plugins=stage_plugins)
- self.inplanes = planes * self.block.expansion
- layer_name = f'layer{i + 1}'
- self.add_module(layer_name, res_layer)
- self.res_layers.append(layer_name)
-
- self._freeze_stages()
-
- self.feat_dim = self.block.expansion * base_channels * 2**(
- len(self.stage_blocks) - 1)
-
- def make_stage_plugins(self, plugins, stage_idx):
- """Make plugins for ResNet ``stage_idx`` th stage.
-
- Currently we support to insert ``context_block``,
- ``empirical_attention_block``, ``nonlocal_block`` into the backbone
- like ResNet/ResNeXt. They could be inserted after conv1/conv2/conv3 of
- Bottleneck.
-
- An example of plugins format could be:
-
- Examples:
- >>> plugins=[
- ... dict(cfg=dict(type='xxx', arg1='xxx'),
- ... stages=(False, True, True, True),
- ... position='after_conv2'),
- ... dict(cfg=dict(type='yyy'),
- ... stages=(True, True, True, True),
- ... position='after_conv3'),
- ... dict(cfg=dict(type='zzz', postfix='1'),
- ... stages=(True, True, True, True),
- ... position='after_conv3'),
- ... dict(cfg=dict(type='zzz', postfix='2'),
- ... stages=(True, True, True, True),
- ... position='after_conv3')
- ... ]
- >>> self = ResNet(depth=18)
- >>> stage_plugins = self.make_stage_plugins(plugins, 0)
- >>> assert len(stage_plugins) == 3
-
- Suppose ``stage_idx=0``, the structure of blocks in the stage would be:
-
- .. code-block:: none
-
- conv1-> conv2->conv3->yyy->zzz1->zzz2
-
- Suppose 'stage_idx=1', the structure of blocks in the stage would be:
-
- .. code-block:: none
-
- conv1-> conv2->xxx->conv3->yyy->zzz1->zzz2
-
- If stages is missing, the plugin would be applied to all stages.
-
- Args:
- plugins (list[dict]): List of plugins cfg to build. The postfix is
- required if multiple same type plugins are inserted.
- stage_idx (int): Index of stage to build
-
- Returns:
- list[dict]: Plugins for current stage
- """
- stage_plugins = []
- for plugin in plugins:
- plugin = plugin.copy()
- stages = plugin.pop('stages', None)
- assert stages is None or len(stages) == self.num_stages
- # whether to insert plugin into current stage
- if stages is None or stages[stage_idx]:
- stage_plugins.append(plugin)
-
- return stage_plugins
-
- def make_res_layer(self, **kwargs):
- """Pack all blocks in a stage into a ``ResLayer``."""
- return ResLayer(**kwargs)
-
- @property
- def norm1(self):
- """nn.Module: the normalization layer named "norm1" """
- return getattr(self, self.norm1_name)
-
- def _make_stem_layer(self, in_channels, stem_channels):
- if self.deep_stem:
- self.stem = nn.Sequential(
- build_conv_layer(
- self.conv_cfg,
- in_channels,
- stem_channels // 2,
- kernel_size=3,
- stride=2,
- padding=1,
- bias=False),
- build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
- nn.ReLU(inplace=True),
- build_conv_layer(
- self.conv_cfg,
- stem_channels // 2,
- stem_channels // 2,
- kernel_size=3,
- stride=1,
- padding=1,
- bias=False),
- build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
- nn.ReLU(inplace=True),
- build_conv_layer(
- self.conv_cfg,
- stem_channels // 2,
- stem_channels,
- kernel_size=3,
- stride=1,
- padding=1,
- bias=False),
- build_norm_layer(self.norm_cfg, stem_channels)[1],
- nn.ReLU(inplace=True))
- else:
- self.conv1 = build_conv_layer(
- self.conv_cfg,
- in_channels,
- stem_channels,
- kernel_size=7,
- stride=2,
- padding=3,
- bias=False)
- self.norm1_name, norm1 = build_norm_layer(
- self.norm_cfg, stem_channels, postfix=1)
- self.add_module(self.norm1_name, norm1)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
- def _freeze_stages(self):
- if self.frozen_stages >= 0:
- if self.deep_stem:
- self.stem.eval()
- for param in self.stem.parameters():
- param.requires_grad = False
- else:
- self.norm1.eval()
- for m in [self.conv1, self.norm1]:
- for param in m.parameters():
- param.requires_grad = False
-
- for i in range(1, self.frozen_stages + 1):
- m = getattr(self, f'layer{i}')
- m.eval()
- for param in m.parameters():
- param.requires_grad = False
-
- def init_weights(self, pretrained=None):
- """Initialize the weights in backbone.
-
- Args:
- pretrained (str, optional): Path to pre-trained weights.
- Defaults to None.
- """
- if isinstance(pretrained, str):
- logger = get_root_logger()
- load_checkpoint(self, pretrained, strict=False, logger=logger)
- elif pretrained is None:
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- kaiming_init(m)
- elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
- constant_init(m, 1)
-
- if self.dcn is not None:
- for m in self.modules():
- if isinstance(m, Bottleneck) and hasattr(
- m.conv2, 'conv_offset'):
- constant_init(m.conv2.conv_offset, 0)
-
- if self.zero_init_residual:
- for m in self.modules():
- if isinstance(m, Bottleneck):
- constant_init(m.norm3, 0)
- elif isinstance(m, BasicBlock):
- constant_init(m.norm2, 0)
- else:
- raise TypeError('pretrained must be a str or None')
-
- def forward(self, x):
- """Forward function."""
- if self.deep_stem:
- x = self.stem(x)
- else:
- x = self.conv1(x)
- x = self.norm1(x)
- x = self.relu(x)
- x = self.maxpool(x)
- outs = []
- for i, layer_name in enumerate(self.res_layers):
- res_layer = getattr(self, layer_name)
- x = res_layer(x)
- if i in self.out_indices:
- outs.append(x)
- return tuple(outs)
-
- def train(self, mode=True):
- """Convert the model into training mode while keep normalization layer
- freezed."""
- super(ResNet, self).train(mode)
- self._freeze_stages()
- if mode and self.norm_eval:
- for m in self.modules():
- # trick: eval have effect on BatchNorm only
- if isinstance(m, _BatchNorm):
- m.eval()
-
-
-@BACKBONES.register_module()
-class ResNetV1d(ResNet):
- r"""ResNetV1d variant described in `Bag of Tricks
-
-
-
-
- 
-
-- -We use 20K hours of licensed music to train MusicGen. Specifically, we rely on an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music data. - -## Installation -Audiocraft requires Python 3.9, PyTorch 2.0.0, and a GPU with at least 16 GB of memory (for the medium-sized model). To install Audiocraft, you can run the following: - -```shell -# Best to make sure you have torch installed first, in particular before installing xformers. -# Don't run this if you already have PyTorch installed. -pip install 'torch>=2.0' -# Then proceed to one of the following -pip install -U audiocraft # stable release -pip install -U git+https://git@github.com/GrandaddyShmax/audiocraft_plus#egg=audiocraft -pip install -e . # or if you cloned the repo locally -``` - -## Usage -We offer a number of way to interact with MusicGen+: -1. MusicGen+ is also available on the [`GrandaddyShmax/MusicGen_Plus` HuggingFace Space](https://huggingface.co/spaces/GrandaddyShmax/MusicGen_Plus), -2. You can run MusicGen+ on a Colab: [colab notebook](https://colab.research.google.com/github/camenduru/MusicGen-colab/blob/main/MusicGen_ClownOfMadness_plus_colab.ipynb). -3. You can use the gradio MusicGen+ locally by running `python app.py`. -4. Checkout [@camenduru Colab page](https://github.com/camenduru/MusicGen-colab) which is regularly - updated with contributions from @camenduru and the community. -5. Finally, MusicGen is available in 🤗 Transformers from v4.31.0 onwards, see section [🤗 Transformers Usage](#-transformers-usage) below. - -## API - -We provide a simple API and 4 pre-trained models. The pre trained models are: -- `small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small) -- `medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium) -- `melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody) -- `large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large) - -We observe the best trade-off between quality and compute with the `medium` or `melody` model. -In order to use MusicGen locally **you must have a GPU**. We recommend 16GB of memory, but smaller -GPUs will be able to generate short sequences, or longer sequences with the `small` model. - -**Note**: Please make sure to have [ffmpeg](https://ffmpeg.org/download.html) installed when using newer version of `torchaudio`. -You can install it with: -``` -apt-get install ffmpeg -``` - -See after a quick example for using the API. - -```python -import torchaudio -from audiocraft.models import MusicGen -from audiocraft.data.audio import audio_write - -model = MusicGen.get_pretrained('melody') -model.set_generation_params(duration=8) # generate 8 seconds. -wav = model.generate_unconditional(4) # generates 4 unconditional audio samples -descriptions = ['happy rock', 'energetic EDM', 'sad jazz'] -wav = model.generate(descriptions) # generates 3 samples. - -melody, sr = torchaudio.load('./assets/bach.mp3') -# generates using the melody from the given audio and the provided descriptions. -wav = model.generate_with_chroma(descriptions, melody[None].expand(3, -1, -1), sr) - -for idx, one_wav in enumerate(wav): - # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. - audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True) -``` - -## 🤗 Transformers Usage - -MusicGen is available in the 🤗 Transformers library from version 4.31.0 onwards, requiring minimal dependencies -and additional packages. Steps to get started: - -1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) from main: - -``` -pip install git+https://github.com/huggingface/transformers.git -``` - -2. Run the following Python code to generate text-conditional audio samples: - -```py -from transformers import AutoProcessor, MusicgenForConditionalGeneration - - -processor = AutoProcessor.from_pretrained("facebook/musicgen-small") -model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small") - -inputs = processor( - text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"], - padding=True, - return_tensors="pt", -) - -audio_values = model.generate(**inputs, max_new_tokens=256) -``` - -3. Listen to the audio samples either in an ipynb notebook: - -```py -from IPython.display import Audio - -sampling_rate = model.config.audio_encoder.sampling_rate -Audio(audio_values[0].numpy(), rate=sampling_rate) -``` - -Or save them as a `.wav` file using a third-party library, e.g. `scipy`: - -```py -import scipy - -sampling_rate = model.config.audio_encoder.sampling_rate -scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy()) -``` - -For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the -[MusicGen docs](https://huggingface.co/docs/transformers/main/en/model_doc/musicgen) or the hands-on -[Google Colab](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb). - -## Model Card - -See [the model card page](./MODEL_CARD.md). - -## FAQ - -#### Will the training code be released? - -Yes. We will soon release the training code for MusicGen and EnCodec. - - -#### I need help on Windows - -@FurkanGozukara made a complete tutorial for [Audiocraft/MusicGen on Windows](https://youtu.be/v-YpvPkhdO4) - -#### I need help for running the demo on Colab - -Check [@camenduru tutorial on Youtube](https://www.youtube.com/watch?v=EGfxuTy9Eeo). - - -## Citation -``` -@article{copet2023simple, - title={Simple and Controllable Music Generation}, - author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, - year={2023}, - journal={arXiv preprint arXiv:2306.05284}, -} -``` - -## License -* The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE). -* The weights in this repository are released under the CC-BY-NC 4.0 license as found in the [LICENSE_weights file](LICENSE_weights). - -[arxiv]: https://arxiv.org/abs/2306.05284 -[musicgen_samples]: https://ai.honu.io/papers/musicgen/ diff --git a/spaces/Grezz/generate_human_motion/VQ-Trans/dataset/prepare/download_model.sh b/spaces/Grezz/generate_human_motion/VQ-Trans/dataset/prepare/download_model.sh deleted file mode 100644 index da32436f6efa93e0c14e1dd52f97068bd75956ab..0000000000000000000000000000000000000000 --- a/spaces/Grezz/generate_human_motion/VQ-Trans/dataset/prepare/download_model.sh +++ /dev/null @@ -1,12 +0,0 @@ - -mkdir -p pretrained -cd pretrained/ - -echo -e "The pretrained model files will be stored in the 'pretrained' folder\n" -gdown 1LaOvwypF-jM2Axnq5dc-Iuvv3w_G-WDE - -unzip VQTrans_pretrained.zip -echo -e "Cleaning\n" -rm VQTrans_pretrained.zip - -echo -e "Downloading done!" \ No newline at end of file diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq_cli/hydra_train.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq_cli/hydra_train.py deleted file mode 100644 index 6555ab415e82c1156c85d000ff030e6107c55d73..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq_cli/hydra_train.py +++ /dev/null @@ -1,88 +0,0 @@ -#!/usr/bin/env python3 -u -# 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 - -from fairseq.dataclass.initialize import add_defaults, hydra_init -from fairseq_cli.train import main as pre_main -from fairseq import distributed_utils, metrics -from fairseq.dataclass.configs import FairseqConfig -from fairseq.dataclass.utils import omegaconf_no_object_check -from fairseq.utils import reset_logging - -import hydra -from hydra.core.hydra_config import HydraConfig -import torch -from omegaconf import OmegaConf, open_dict - - -logger = logging.getLogger("fairseq_cli.hydra_train") - - -@hydra.main(config_path=os.path.join("..", "fairseq", "config"), config_name="config") -def hydra_main(cfg: FairseqConfig) -> float: - _hydra_main(cfg) - - -def _hydra_main(cfg: FairseqConfig, **kwargs) -> float: - add_defaults(cfg) - - if cfg.common.reset_logging: - reset_logging() # Hydra hijacks logging, fix that - else: - # check if directly called or called through hydra_main - if HydraConfig.initialized(): - with open_dict(cfg): - # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) - cfg.job_logging_cfg = OmegaConf.to_container(HydraConfig.get().job_logging, resolve=True) - - with omegaconf_no_object_check(): - cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True, enum_to_str=True)) - OmegaConf.set_struct(cfg, True) - - try: - if cfg.common.profile: - with torch.cuda.profiler.profile(): - with torch.autograd.profiler.emit_nvtx(): - distributed_utils.call_main(cfg, pre_main, **kwargs) - else: - distributed_utils.call_main(cfg, pre_main, **kwargs) - except BaseException as e: - if not cfg.common.suppress_crashes: - raise - else: - logger.error("Crashed! " + str(e)) - - # get best val and return - useful for sweepers - try: - best_val = metrics.get_smoothed_value( - "valid", cfg.checkpoint.best_checkpoint_metric - ) - except: - best_val = None - - if best_val is None: - best_val = float("inf") - - return best_val - - -def cli_main(): - try: - from hydra._internal.utils import get_args - - cfg_name = get_args().config_name or "config" - except: - logger.warning("Failed to get config name from hydra args") - cfg_name = "config" - - hydra_init(cfg_name) - hydra_main() - - -if __name__ == "__main__": - cli_main() diff --git a/spaces/Harveenchadha/oiTrans/app.py b/spaces/Harveenchadha/oiTrans/app.py deleted file mode 100644 index d0379206ffa1c903f104029657247bebcb37f388..0000000000000000000000000000000000000000 --- a/spaces/Harveenchadha/oiTrans/app.py +++ /dev/null @@ -1,52 +0,0 @@ -import os -#import gradio as gr - -os.system('wget -q https://storage.googleapis.com/vakyaansh-open-models/translation_models/indic-en.zip') -os.system('unzip /home/user/app/indic-en.zip') -os.system('wget -q https://storage.googleapis.com/vakyaansh-open-models/translation_models/en-indic.zip') -os.system('unzip /home/user/app/en-indic.zip') -os.system('wget -q https://storage.googleapis.com/vakyaansh-open-models/translation_models/m2m_modified.zip') -os.system('unzip /home/user/app/m2m_modified.zip') -os.system('rm /home/user/app/indic-en.zip') -os.system('rm /home/user/app/en-indic.zip') -os.system('rm /home/user/app/m2m_modified.zip') - -os.system('pip uninstall -y numpy') -os.system('pip install numpy') -#os.system('pip uninstall -y numba') -#os.system('pip install numba==0.53') - -from fairseq import checkpoint_utils, distributed_utils, options, tasks, utils -import gradio as grd -from inference.engine import Model -indic2en_model = Model(expdir='indic-en') -en2indic_model = Model(expdir='en-indic') -indic2indic_model = Model(expdir='m2m') - -INDIC = {"English": "en", "Assamese": "as", "Bengali": "bn", "Gujarati": "gu", "Hindi": "hi","Kannada": "kn","Malayalam": "ml", "Marathi": "mr", "Odia": "or","Punjabi": "pa","Tamil": "ta", "Telugu" : "te" } - - -def translate(text, source_lang, dest_lang): - if source_lang == 'en': - return en2indic_model.translate_paragraph(text, 'en', INDIC[dest_lang]) - elif dest_lang == 'en': - return indic2en_model.translate_paragraph(text, INDIC[source_lang], 'en') - else: - return indic2indic_model.translate_paragraph(text, INDIC[source_lang], INDIC[dest_lang]) - - -languages = list(INDIC.keys()) - -#print(translate('helo how are you')) -ddwn_src = grd.inputs.Dropdown(languages, type="value", default="English", label="Select Source Language") -ddwn_tar = grd.inputs.Dropdown(languages, type="value", default="Hindi", label="Select Target Language") - -txt = grd.inputs.Textbox( lines=5, placeholder="Enter Text to translate", default="", label="Enter Text in Source Language") -txt_ouptut = grd.outputs.Textbox(type="auto", label="Translated text in English Language") - -example=[['मैं इस वाक्य का हिंदी में अनुवाद करना चाहता हूं','Hindi', 'English'], - ['আজ আমার খুব ভালো লাগছে', 'Bengali' , 'Hindi']] - -supp = ','.join(languages) -iface = grd.Interface(fn=translate, inputs=[txt,ddwn_src,ddwn_tar] , outputs=txt_ouptut, title='Translation for 11 Indic Languages', description = 'This is a demo based on IndicTrans. Languages Supported: '+supp, article = 'Original repo [link](https://github.com/AI4Bharat/indicTrans) by AI4Bharat. Note: This is an attempt to create open version of Translation for Indic languages. ', examples=example) -iface.launch(enable_queue=True) diff --git a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/setup.py b/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/setup.py deleted file mode 100644 index a045b763fb4a4f61bac23b735544a18ffc68d20a..0000000000000000000000000000000000000000 --- a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/setup.py +++ /dev/null @@ -1,208 +0,0 @@ -# coding=utf-8 -# Copyright 2022 The IDEA 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. -# ------------------------------------------------------------------------------------------------ -# Modified from -# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/setup.py -# https://github.com/facebookresearch/detectron2/blob/main/setup.py -# https://github.com/open-mmlab/mmdetection/blob/master/setup.py -# https://github.com/Oneflow-Inc/libai/blob/main/setup.py -# ------------------------------------------------------------------------------------------------ - -import glob -import os -import subprocess - -import torch -from setuptools import find_packages, setup -from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension - -# groundingdino version info -version = "0.1.0" -package_name = "groundingdino" -cwd = os.path.dirname(os.path.abspath(__file__)) - - -sha = "Unknown" -try: - sha = subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=cwd).decode("ascii").strip() -except Exception: - pass - - -def write_version_file(): - version_path = os.path.join(cwd, "groundingdino", "version.py") - with open(version_path, "w") as f: - f.write(f"__version__ = '{version}'\n") - # f.write(f"git_version = {repr(sha)}\n") - - -requirements = ["torch", "torchvision"] - -torch_ver = [int(x) for x in torch.__version__.split(".")[:2]] - - -def get_extensions(): - this_dir = os.path.dirname(os.path.abspath(__file__)) - extensions_dir = os.path.join(this_dir, "groundingdino", "models", "GroundingDINO", "csrc") - - main_source = os.path.join(extensions_dir, "vision.cpp") - sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp")) - source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu")) + glob.glob( - os.path.join(extensions_dir, "*.cu") - ) - - sources = [main_source] + sources - - extension = CppExtension - - extra_compile_args = {"cxx": []} - define_macros = [] - - if torch.cuda.is_available() and CUDA_HOME is not None: - print("Compiling with CUDA") - extension = CUDAExtension - sources += source_cuda - define_macros += [("WITH_CUDA", None)] - extra_compile_args["nvcc"] = [ - "-DCUDA_HAS_FP16=1", - "-D__CUDA_NO_HALF_OPERATORS__", - "-D__CUDA_NO_HALF_CONVERSIONS__", - "-D__CUDA_NO_HALF2_OPERATORS__", - ] - else: - print("Compiling without CUDA") - define_macros += [("WITH_HIP", None)] - extra_compile_args["nvcc"] = [] - return None - - sources = [os.path.join(extensions_dir, s) for s in sources] - include_dirs = [extensions_dir] - - ext_modules = [ - extension( - "groundingdino._C", - sources, - include_dirs=include_dirs, - define_macros=define_macros, - extra_compile_args=extra_compile_args, - ) - ] - - return ext_modules - - -def parse_requirements(fname="requirements.txt", with_version=True): - """Parse the package dependencies listed in a requirements file but strips - specific versioning information. - - Args: - fname (str): path to requirements file - with_version (bool, default=False): if True include version specs - - Returns: - List[str]: list of requirements items - - CommandLine: - python -c "import setup; print(setup.parse_requirements())" - """ - import re - import sys - from os.path import exists - - require_fpath = fname - - def parse_line(line): - """Parse information from a line in a requirements text file.""" - if line.startswith("-r "): - # Allow specifying requirements in other files - target = line.split(" ")[1] - for info in parse_require_file(target): - yield info - else: - info = {"line": line} - if line.startswith("-e "): - info["package"] = line.split("#egg=")[1] - elif "@git+" in line: - info["package"] = line - else: - # Remove versioning from the package - pat = "(" + "|".join([">=", "==", ">"]) + ")" - parts = re.split(pat, line, maxsplit=1) - parts = [p.strip() for p in parts] - - info["package"] = parts[0] - if len(parts) > 1: - op, rest = parts[1:] - if ";" in rest: - # Handle platform specific dependencies - # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies - version, platform_deps = map(str.strip, rest.split(";")) - info["platform_deps"] = platform_deps - else: - version = rest # NOQA - info["version"] = (op, version) - yield info - - def parse_require_file(fpath): - with open(fpath, "r") as f: - for line in f.readlines(): - line = line.strip() - if line and not line.startswith("#"): - for info in parse_line(line): - yield info - - def gen_packages_items(): - if exists(require_fpath): - for info in parse_require_file(require_fpath): - parts = [info["package"]] - if with_version and "version" in info: - parts.extend(info["version"]) - if not sys.version.startswith("3.4"): - # apparently package_deps are broken in 3.4 - platform_deps = info.get("platform_deps") - if platform_deps is not None: - parts.append(";" + platform_deps) - item = "".join(parts) - yield item - - packages = list(gen_packages_items()) - return packages - - -if __name__ == "__main__": - print(f"Building wheel {package_name}-{version}") - - with open("LICENSE", "r", encoding="utf-8") as f: - license = f.read() - - write_version_file() - - setup( - name="groundingdino", - version="0.1.0", - author="International Digital Economy Academy, Shilong Liu", - url="https://github.com/IDEA-Research/GroundingDINO", - description="open-set object detector", - license=license, - install_requires=parse_requirements("requirements.txt"), - packages=find_packages( - exclude=( - "configs", - "tests", - ) - ), - ext_modules=get_extensions(), - cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension}, - ) diff --git a/spaces/Iceclear/StableSR/README.md b/spaces/Iceclear/StableSR/README.md deleted file mode 100644 index 06090b4e9f22dc767199046b58944186b9dc45bf..0000000000000000000000000000000000000000 --- a/spaces/Iceclear/StableSR/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: StableSR -emoji: 😻 -colorFrom: purple -colorTo: indigo -sdk: gradio -sdk_version: 3.40.1 -app_file: app.py -pinned: false -license: other ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Illumotion/Koboldcpp/examples/llama2.sh b/spaces/Illumotion/Koboldcpp/examples/llama2.sh deleted file mode 100644 index 221b37553cfe7d825a9666d8d5c337a7814a3c64..0000000000000000000000000000000000000000 --- a/spaces/Illumotion/Koboldcpp/examples/llama2.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \ - --color \ - --ctx_size 2048 \ - -n -1 \ - -ins -b 256 \ - --top_k 10000 \ - --temp 0.2 \ - --repeat_penalty 1.1 \ - -t 8 diff --git a/spaces/Illumotion/Koboldcpp/examples/parallel/README.md b/spaces/Illumotion/Koboldcpp/examples/parallel/README.md deleted file mode 100644 index 4d0fe5cef12fa7e2d28e18571ba94d290e47999b..0000000000000000000000000000000000000000 --- a/spaces/Illumotion/Koboldcpp/examples/parallel/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# llama.cpp/example/parallel - -Simplified simluation for serving incoming requests in parallel diff --git a/spaces/Illumotion/Koboldcpp/make_pyinstaller.bat b/spaces/Illumotion/Koboldcpp/make_pyinstaller.bat deleted file mode 100644 index 5ca9604192c2a958a0ea1b102c3a64351b842147..0000000000000000000000000000000000000000 --- a/spaces/Illumotion/Koboldcpp/make_pyinstaller.bat +++ /dev/null @@ -1 +0,0 @@ -PyInstaller --noconfirm --onefile --clean --console --collect-all customtkinter --icon "./niko.ico" --add-data "./klite.embd;." --add-data "./koboldcpp_default.dll;." --add-data "./koboldcpp_openblas.dll;." --add-data "./koboldcpp_failsafe.dll;." --add-data "./koboldcpp_noavx2.dll;." --add-data "./libopenblas.dll;." --add-data "./koboldcpp_clblast.dll;." --add-data "./clblast.dll;." --add-data "./rwkv_vocab.embd;." --add-data "./rwkv_world_vocab.embd;." "./koboldcpp.py" -n "koboldcpp.exe" \ No newline at end of file diff --git a/spaces/Illumotion/Koboldcpp/otherarch/tools/neox_quantize.cpp b/spaces/Illumotion/Koboldcpp/otherarch/tools/neox_quantize.cpp deleted file mode 100644 index 602c9d3c49e3efb5a25b1cc3c3b0b76e8a5644d6..0000000000000000000000000000000000000000 --- a/spaces/Illumotion/Koboldcpp/otherarch/tools/neox_quantize.cpp +++ /dev/null @@ -1,179 +0,0 @@ -#include "ggml.h" - -#include "utils.h" -#include "common-ggml.h" - -#include
-
-
-