V-Ray Next rendering modes and options
-V-Ray Next offers you different rendering modes and options depending on your needs and preferences. You can choose between:
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-
- Production rendering, which is the standard mode for creating high-quality images or animations with all the features and settings available in V-Ray. -
- Interactive rendering, which is a fast and responsive mode that updates your image as you make changes to your scene, camera, lights, materials, etc. This mode is ideal for testing and previewing your scene before production rendering. -
- GPU rendering, which is a mode that uses your graphics card (GPU) instead of your processor (CPU) to render your scene. This mode can be faster and more efficient for certain types of scenes and effects, such as volumetrics, denoising, etc. -
- Hybrid rendering, which is a mode that combines both CPU and GPU rendering to utilize all your hardware resources and speed up your rendering process. -
You can switch between these modes and options in the V-Ray Render Settings window, under the Renderer rollout. You can also adjust various parameters related to sampling, ray tracing, global illumination, motion blur, depth of field, etc.
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V-Ray Next lighting and materials
-V-Ray Next provides you with a wide range of lighting and material options to create realistic and stunning scenes. You can use:
--
-
- V-Ray lights, which are special types of lights that are optimized for V-Ray rendering. You can create different kinds of V-Ray lights such as dome light, sun light, sky light, sphere light, rectangle light, mesh light, etc. -
- V-Ray materials, which are special types of materials that are optimized for V-Ray rendering. You can create different kinds of V-Ray materials such as standard material, blend material, car paint material, hair material, subsurface scattering material, etc. -
- V-Ray textures, which are special types of textures that are optimized for V-Ray rendering. You can use different kinds of V-Ray textures such as bitmap texture, noise texture, gradient texture, dirt texture, curvature texture, etc. -
- V-Ray shaders, which are special types of nodes that can be used to create custom effects and functions for your materials and textures. You can use different kinds of V-Ray shaders such as color correction shader, falloff shader, fresnel shader, triplanar shader, etc. -
You can manage all your lighting and material assets in the V-Ray Asset Editor window, where you can create, edit, assign, organize, and preview them. You can also import and export assets from external sources such as Substance Designer or PBR materials.
-V-Ray Next effects and post-processing
-V-Ray Next allows you to add various effects and post-processing adjustments to your rendered image or animation without leaving 3ds Max or using external applications. You can use:
--
-
- V-Ray render elements, which are separate layers or channels of your rendered image or animation that can be used for compositing or post-processing. You can create different kinds of V-Ray render elements such as diffuse element, specular element, reflection element, refraction element, shadow element, lighting element, etc.
- V-Ray frame buffer tools, which are tools and controls that you can use to modify your rendered image or animation in the VFB window. You can use different kinds of VFB tools such as color corrections tool, lens effects tool, denoiser tool, history tool, statistics tool, etc.
You can access all your render elements and frame buffer tools in the VFB window, where you can enable, disable, edit, save, load, and compare them. You can also export them to external applications such as Photoshop or Nuke.
How to optimize V-Ray Next for 3ds Max? -V-Ray Next scene intelligence
-V-Ray Next introduces a new feature called scene intelligence that automatically analyzes your scene and optimizes your rendering settings accordingly. Scene intelligence includes:
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-
- Automatic exposure and white balance, which adjusts the camera exposure and color temperature based on the lighting conditions of your scene. This feature eliminates the need for manual tweaking and ensures a balanced and realistic image. -
- Adaptive dome light, which samples only the parts of the dome light that contribute to the illumination of your scene. This feature speeds up your rendering time by up to 7 times for scenes with image-based lighting. -
- Point-and-shoot camera, which sets the camera focus distance automatically based on where you click in the viewport. This feature simplifies the process of creating depth of field effects. -
- Automatic memory management, which optimizes the memory usage of your scene by dynamically loading and unloading assets as needed. This feature allows you to render large scenes with complex geometries and textures without running out of memory. -
You can enable or disable these features in the V-Ray Render Settings window, under the Camera rollout (for automatic exposure and white balance), Environment rollout (for adaptive dome light), Physical Camera rollout (for point-and-shoot camera), and System rollout (for automatic memory management).
-V-Ray Next adaptive dome light
-V-Ray Next introduces a new feature called adaptive dome light that automatically samples only the parts of the dome light that contribute to the illumination of your scene. This feature speeds up your rendering time by up to 7 times for scenes with image-based lighting.
-V-Ray Next GPU rendering and denoising
-V-Ray Next introduces a new feature called GPU rendering and denoising that allows you to use your graphics card (GPU) instead of your processor (CPU) to render your scene and remove noise from your image or animation. This feature can be faster and more efficient for certain types of scenes and effects, such as volumetrics, denoising, etc.
-GPU rendering is a mode that uses your graphics card (GPU) instead of your processor (CPU) to render your scene. This mode can be faster and more efficient for certain types of scenes and effects, such as volumetrics, denoising, etc. You can switch to GPU rendering in the V-Ray Render Settings window, under the Renderer rollout, by choosing CUDA or RTX as the engine type. You can also select which GPUs or CPUs you want to use for rendering.
-Denoising is a process that removes noise from your image or animation without losing detail or quality. Noise is a common problem in rendering, especially when using low sampling settings or complex lighting scenarios. V-Ray Next offers you different options for denoising, such as:
--
-
- V-Ray Denoiser, which is a built-in denoiser that works on both CPU and GPU rendering modes. You can enable it in the V-Ray Render Settings window, under the V-Ray Denoiser rollout. You can also adjust various parameters related to quality, blend amount, radius, etc. -
- NVIDIA AI Denoiser, which is an external denoiser that works only on GPU rendering mode. You can enable it in the VFB window, by clicking on its icon in the VFB toolbar. This denoiser uses artificial intelligence to remove noise instantly and interactively. -
- Render element denoiser, which is a new feature that allows you to denoise individual render elements separately. You can enable it in the Render Elements tab of the Render Setup window, by checking the Denoise option for each render element. This feature ensures that the denoised render elements match the denoised beauty image. -
You can access all your denoising options and tools in the VFB window, where you can enable, disable, edit, save, load, and compare them. You can also export them to external applications such as Photoshop or Nuke.
-Conclusion
-Summary of the main points
-In this article, we have introduced you to V-Ray Next ADV v4.30.01 for 3ds Max 2013-2020 Win x64, which is the latest version of V-Ray for 3ds Max. We have shown you how to install it, how to use it, and how to optimize it for your workflow and projects. We have also highlighted some of the new features and improvements that make V-Ray Next faster, smarter, and more powerful than ever before.
-Some of the main features and improvements of V-Ray Next are:
--
-
- Scene intelligence, which automatically analyzes your scene and optimizes your rendering settings accordingly. -
- Adaptive dome light, which samples only the parts of the dome light that contribute to the illumination of your scene. -
- GPU rendering and denoising, which allows you to use your graphics card (GPU) instead of your processor (CPU) to render your scene and remove noise from your image or animation. -
- Render element denoiser, which allows you to denoise individual render elements separately. -
- New lighting analysis tools, which make it easier to visualize a scene’s real-world illumination values in lux or footcandles. -
- New metalness material properties, which offer improved compatibility with Substance Designer and PBR materials. -
Call to action and links
-If you are interested in trying out V-Ray Next for 3ds Max yourself, you can download a free 30-day trial from the official website. You will need to register or log in with your Chaos account to access the download link.
-If you want to learn more about V-Ray Next for 3ds Max, you can visit the following links:
--
-
- V-Ray Next for 3ds Max Help Home, which contains detailed documentation and tutorials on how to use V-Ray Next for 3ds Max. -
- V-Ray for 3ds Max Blog, which features news, updates, tips, tricks, and showcases on V-Ray for 3ds Max. -
- V-Ray Academy, which offers online courses and webinars on V-Ray for 3ds Max and other Chaos products. -
- V-Ray for 3ds Max Forum, which is a place where you can ask questions, get answers, share feedback, and connect with other V-Ray users.
We hope you enjoyed this article and found it useful. If you have any comments or suggestions, please let us know in the comments section below. Thank you for reading!
FAQs -What is V-Ray Next?
-V-Ray Next is the latest version of V-Ray for 3ds Max, which is a powerful and versatile rendering plugin that can handle any type of project, from architectural visualization to visual effects.
What are the main features of V-Ray Next? -Some of the main features of V-Ray Next are scene intelligence, adaptive dome light, GPU rendering and denoising, render element denoiser, new lighting analysis tools, and new metalness material properties.
How to install V-Ray Next for 3ds Max? -To install V-Ray Next for 3ds Max, you need to download the installer from the official website, run it as administrator, follow the instructions on the screen, uninstall any previous versions of V-Ray, and activate your license online or offline.
How to use V-Ray Next for 3ds Max? -To use V-Ray Next for 3ds Max, you need to switch the renderer to V-Ray GPU in the Render Setup window, access various commands and options from the V-Ray menu, manage your assets in the V-Ray Asset Editor window, adjust your settings in the V-Ray Render Settings window, and view your results in the VFB window.
How to optimize V-Ray Next for 3ds Max? -To optimize V-Ray Next for 3ds Max, you need to enable or disable various features in the Render Setup window, such as automatic exposure and white balance, adaptive dome light, GPU rendering and denoising, and automatic memory management.
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- -Our understanding of mother culture has to do with a lot of work, sustained over many generations, of feeding children and supporting them emotionally, physically, intellectually, culturally, spiritually and psychologically and that is what is being lost. This film tries to understand the powerful culture of India, how it is integral to us and then what happens when there are no families and children left to support it. This piece of work is made in the hope that other directors, who work in film and television, will be inspired and will make works in this genre that respect that culture and make a small contribution to saving it. - -Ajantha Matiyar: So, there is this curious notion that you are trying to depict a dying culture. What is it that makes it dying? - -Sarojini Rikhye: The urban-based Bengali culture that you see around you when you step out of the airport, the metro station or the bus stand -- that is not just about Bengali culture but that is an indicator of what is happening all over India. It is the culture of the housewife, of the woman who has no work and has to be ‘put in a position’ to have children, and we need to understand that this phenomenon is not a Bengali phenomenon. It is happening all over India. The culture of so many generations of women and children has been trying to support their families through a woman’s right to her own body. They have no land to get agricultural produce from, to sell. There is no daycare centre for the children, no schools for the children, no health centres, no spaces for the men to socialise and their entire value system is against women. As a result of this, the children are faced with a lot of physical and mental agony and the women are facing depression and other mental health issues. - -AM: But, what is interesting is that, while many of us have heard about so many negative things that are happening in India, we do not know that this culture is facing a crisis. - -SR: The focus should be on the culture of the society, not just on this one thing or another. - -AM: So, what is it that makes it dying? - -SR: A lot of change that you see in Bengal since the 1960s has been the result of educated, urban middle-class, mobile and progressive young women, who came from all over Bengal, from all over India, from other cities and towns, and came to Bengal 4fefd39f24
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- diff --git a/spaces/1line/AutoGPT/autogpt/spinner.py b/spaces/1line/AutoGPT/autogpt/spinner.py deleted file mode 100644 index 4e33d74213881352546f334ccb1eb4772b8b7b70..0000000000000000000000000000000000000000 --- a/spaces/1line/AutoGPT/autogpt/spinner.py +++ /dev/null @@ -1,65 +0,0 @@ -"""A simple spinner module""" -import itertools -import sys -import threading -import time - - -class Spinner: - """A simple spinner class""" - - def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None: - """Initialize the spinner class - - Args: - message (str): The message to display. - delay (float): The delay between each spinner update. - """ - self.spinner = itertools.cycle(["-", "/", "|", "\\"]) - self.delay = delay - self.message = message - self.running = False - self.spinner_thread = None - - def spin(self) -> None: - """Spin the spinner""" - while self.running: - sys.stdout.write(f"{next(self.spinner)} {self.message}\r") - sys.stdout.flush() - time.sleep(self.delay) - sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r") - - def __enter__(self): - """Start the spinner""" - self.running = True - self.spinner_thread = threading.Thread(target=self.spin) - self.spinner_thread.start() - - return self - - def __exit__(self, exc_type, exc_value, exc_traceback) -> None: - """Stop the spinner - - Args: - exc_type (Exception): The exception type. - exc_value (Exception): The exception value. - exc_traceback (Exception): The exception traceback. - """ - self.running = False - if self.spinner_thread is not None: - self.spinner_thread.join() - sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r") - sys.stdout.flush() - - def update_message(self, new_message, delay=0.1): - """Update the spinner message - Args: - new_message (str): New message to display - delay: Delay in seconds before updating the message - """ - time.sleep(delay) - sys.stdout.write( - f"\r{' ' * (len(self.message) + 2)}\r" - ) # Clear the current message - sys.stdout.flush() - self.message = new_message diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Getting Over It for PC - The Ultimate Challenge Game from Ocean of Games.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Getting Over It for PC - The Ultimate Challenge Game from Ocean of Games.md deleted file mode 100644 index 6032c1577ea0187183cb55b458655e646d1251a5..0000000000000000000000000000000000000000 --- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Getting Over It for PC - The Ultimate Challenge Game from Ocean of Games.md +++ /dev/null @@ -1,133 +0,0 @@ - -
Download Getting Over It PC Ocean of Games: A Guide to the Most Frustrating Game Ever
-Have you ever played a game that made you want to smash your keyboard, throw your mouse, or scream at your monitor? If not, then you might want to try Getting Over It with Bennett Foddy, a game that is designed to hurt you. And if you are looking for a way to download this game for free, then you might be interested in Ocean of Games, a website that offers a variety of PC games for download. But before you do that, let's take a look at what Getting Over It is, why it is so frustrating, how to download it from Ocean of Games, and how to beat it.
-What is Getting Over It?
-Getting Over It with Bennett Foddy is an action game that was released in 2017 for Windows, Mac, iOS, and Android. It is developed by Bennett Foddy, an independent game designer and academic who is known for creating experimental and challenging games such as QWOP, GIRP, and CLOP.
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The premise and the gameplay
-The premise of Getting Over It is simple: you are a man named Diogenes who is stuck in a cauldron and has a hammer as his only tool. Your goal is to climb up an enormous mountain made of various objects such as rocks, trees, furniture, pipes, and even other games. You move the hammer with the mouse or the touch screen, and that's all there is. There are no checkpoints, no saves, no levels, no scores, no achievements. Just you and the mountain.
-The gameplay of Getting Over It is deceptively simple as well. You can use the hammer to push yourself off the ground, hook onto objects, swing around them, or launch yourself into the air. With practice, you'll be able to jump, swing, climb, and fly. However, this is easier said than done. The game's physics are realistic but unforgiving. A slight mistake can send you tumbling down the mountain, losing all your progress in an instant. And there is nothing to stop you from falling all the way back to the beginning.
-The developer and the inspiration
-Bennett Foddy is an Australian-born game designer who is currently a professor at New York University's Game Center. He has a PhD in philosophy from Oxford University and has written several papers on topics such as ethics, aesthetics, and game design. He is also a musician who plays bass guitar for the band Cut Copy.
-Foddy has stated that he made Getting Over It as a tribute to Jazzuo's 2002 B-Game classic Sexy Hiking, which has a similar concept of climbing a mountain with a hammer. He also said that he wanted to make a game for a certain kind of person: someone who likes hard games, who likes frustration games, who likes roguelikes, who likes speedrunning. He wanted to make a game that would hurt them.
-The reception and the reviews
-Getting Over It has received mostly positive reviews from critics and players alike. It has a score of 77/100 on Metacritic and an \"Overwhelmingly Positive\" rating on Steam. Many reviewers praised the game's originality, humor, challenge, and satisfaction. They also appreciated the game's commentary by F
oddy, who provides insights, jokes, quotes, and encouragement throughout the game. Some reviewers also noted that the game can be seen as a metaphor for life, art, or game development itself.
-However, not everyone enjoyed Getting Over It. Some reviewers criticized the game's difficulty, frustration, and repetitiveness. They also complained about the game's lack of features, options, and accessibility. Some players also reported technical issues, bugs, and crashes. And of course, some players simply hated the game for making them rage quit.
-Why is Getting Over It so frustrating?
-Getting Over It is not a game for everyone. It is a game that tests your patience, skill, and sanity. It is a game that can make you feel angry, sad, hopeless, or even depressed. But why is it so frustrating? Here are some of the reasons:
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The controls and the physics
-The controls of Getting Over It are simple but hard to master. You only need to move the mouse or the touch screen to control the hammer, but that's easier said than done. The hammer's movement is sensitive and precise, but also erratic and unpredictable. You need to have a good sense of timing, distance, and angle to move effectively. And you need to constantly adjust your grip and position to avoid losing balance or momentum.
-The physics of Getting Over It are realistic but unforgiving. The game simulates gravity, friction, inertia, and collision in a realistic way, but that also means that the slightest mistake can have disastrous consequences. You can slip, slide, bounce, or fly off the mountain at any moment. And you can't rely on any safety nets or checkpoints to save you. You have to deal with the consequences of your actions.
-The obstacles and the setbacks
-The obstacles of Getting Over It are varied and challenging. The mountain is made of different objects that have different shapes, sizes, textures, and properties. Some objects are solid and stable, while others are slippery and movable. Some objects are helpful and supportive, while others are harmful and obstructive. Some objects are familiar and recognizable, while others are bizarre and surreal. You never know what to expect next.
-The setbacks of Getting Over It are frequent and painful. The mountain is full of traps and pitfalls that can send you back to where you started or even lower. You can fall from great heights or get stuck in narrow spaces. You can lose hours or days of progress in seconds or minutes. And you have to start over again and again until you reach the top.
-The narration and the commentary
-The narration of Getting Over It is witty but cruel. The game features a voice-over by Bennett Foddy himself, who talks to you throughout the game. He tells you about the history and the design of the game, he quotes from various philosophers and artists, he jokes about your situation and your failures, he encourages you to keep going and to not give up. He also sometimes apologizes for making such a hard game.
-The commentary of Getting Over It is informative but taunting. The game also features a chat box that shows messages from other players who are playing the game at the same time as you. They can share their thoughts, feelings, tips, or jokes with you. They can also see your progress and your falls on their screens. They can cheer you on or mock you mercilessly.
-How to download Getting Over It PC Ocean of Games?
-If you want to play Getting Over It on your PC for free, then you might want to check out Ocean of Games, a website that offers a variety of PC games for download. However, before you do that, you should be aware of some of the advantages and disadvantages of using this website.
-The advantages and disadvantages of Ocean of Games
-Ocean of Games has some advantages over other websites that offer PC games for download. Some of these advantages are:
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- It has a large collection of games from different genres and categories. -
- It has an easy-to-use interface and a fast download speed. -
- It does not require any registration or subscription. -
- It does not have any annoying ads or pop-ups. -
However, Ocean of Games also has some disadvantages that you should be aware of before using it. Some of these disadvantages are:
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- It does not have any official license or authorization from the game developers or publishers. -
- It does not guarantee the quality or the safety of the games it offers. -
- It may contain viruses, malware, spyware, or other harmful software that can damage your PC or compromise your privacy. -
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It may violate the intellectual property rights of the game developers or publishers.
- - It may expose you to legal risks or penalties for piracy or infringement. -
Therefore, you should use Ocean of Games at your own risk and discretion. You should also respect the rights and the work of the game developers or publishers and consider buying the game from official sources if you enjoy it.
-The steps to download and install Getting Over It from Ocean of Games
-If you still want to download Getting Over It PC Ocean of Games, then you can follow these steps:
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- Go to the Ocean of Games website and search for Getting Over It in the search box. -
- Select the game from the list of results and click on the download button. -
- Wait for the download to finish and then extract the zip file to a folder of your choice. -
- Open the folder and run the setup.exe file as an administrator. -
- Follow the instructions on the screen to install the game on your PC. -
- Launch the game from the desktop shortcut or the start menu and enjoy. -
The alternatives to Ocean of Games
-If you are looking for other websites that offer PC games for download, then you might want to check out some of these alternatives:
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- Steam: Steam is the most popular and reputable platform for buying and playing PC games. It has a huge library of games from various genres and categories, as well as features such as cloud saving, achievements, multiplayer, mods, and more. You can also find some free or discounted games on Steam, especially during sales or events. -
- GOG: GOG is another platform that sells and distributes PC games. It specializes in DRM-free games, meaning that you can play them without any online activation or restriction. It also offers some classic and retro games that are compatible with modern systems. -
- itch.io: itch.io is a website that hosts indie games from various developers and creators. You can find some unique and original games on itch.io, as well as some free or pay-what-you-want games. You can also support the developers directly by buying or donating to their games. -
How to beat Getting Over It?
-Getting Over It is a game that is hard to beat, but not impossible. It requires a lot of practice, patience, and perseverance. It also requires some tips, tricks, and strategies. Here are some of them:
-The tips and tricks to master the game
-Here are some tips and tricks that can help you master the game:
--
-
- Learn how to use the hammer effectively. You can use it to push, pull, hook, swing, launch, or balance yourself. Experiment with different movements and angles to find what works best for you. -
- Use both hands to control the mouse or the touch screen. This can give you more precision and stability when moving the hammer. -
- Adjust your mouse sensitivity or touch sensitivity according to your preference. You can do this in the settings menu of the game. You can also adjust your screen resolution or window size to fit your monitor or device. -
- Take breaks regularly. Getting Over It can be mentally and physically exhausting. You should take breaks every 15 minutes or so to relax your eyes, hands, and mind. You can also save your progress by quitting the game and resuming it later. -
- Don't give up. Getting Over It is a game that is meant to challenge you and make you frustrated. But it is also a game that can reward you with satisfaction and accomplishment. Don't let your failures discourage you. Learn from them and try again. -
The speedruns and the records
-If you want to challenge yourself further, you can try to beat Getting Over It as fast as possible. This is called speedrunning, and it is a popular activity among gamers who like to compete with themselves or others. There are many websites and communities that track and showcase speedruns of various games, such as speedrun.com or Speed Demos Archive. You can also watch some videos of speedruns on YouTube or Twitch.
-The current world record for beating Getting Over It is 1 minute 19 seconds by a player named Lockness06. He achieved this feat on June 14th, 2021 using a mouse and keyboard. The previous record was 1 minute 24 seconds by a player named Distortion2. He achieved this feat on May 31st, 2021 using a controller.
-The rewards and the The rewards and the secrets
-
If you manage to beat Getting Over It, you will be rewarded with a special ending that includes a song, a message, and a surprise. We won't spoil it for you, but we can tell you that it is worth the effort. You will also unlock a golden cauldron that you can use to play the game again with a different look.
-Getting Over It also has some secrets and easter eggs that you can discover along the way. Some of them are hidden in the mountain, some of them are triggered by certain actions, and some of them are revealed by the narrator. We won't tell you what they are, but we can give you some hints:
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-
- There is a secret island that you can reach by flying over the ocean. -
- There is a secret room that you can enter by breaking a wall. -
- There is a secret message that you can read by zooming in on a sign. -
- There is a secret mode that you can activate by typing a code. -
- There is a secret game that you can play by clicking on a button. -
Conclusion
-Getting Over It with Bennett Foddy is a game that is not for everyone. It is a game that is hard, frustrating, and sometimes unfair. But it is also a game that is original, humorous, and satisfying. It is a game that challenges you to overcome your limits and to get over it.
-If you want to play Getting Over It on your PC for free, you can download it from Ocean of Games, a website that offers a variety of PC games for download. However, you should be careful of the risks and the drawbacks of using this website. You should also respect the rights and the work of the game developers and publishers and consider buying the game from official sources if you enjoy it.
-If you want to beat Getting Over It, you will need a lot of practice, patience, and perseverance. You will also need some tips, tricks, and strategies. And you will also need to discover some secrets and easter eggs along the way. But most importantly, you will need to have fun and to not give up.
-We hope this guide has helped you to learn more about Getting Over It PC Ocean of Games. If you have any questions or comments, feel free to leave them below. And if you liked this article, please share it with your friends. Thank you for reading and happy climbing!
-FAQs
-Here are some frequently asked questions about Getting Over It PC Ocean of Games:
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- Who is Diogenes? -
- Who is Bennett Foddy? -
- What is Ocean of Games? -
- How long does it take to beat Getting Over It? -
- What are some other games like Getting Over It? -
- Sexy Hiking: The original game that inspired Getting Over It. It has similar gameplay but with worse graphics and sound. -
- Pogostuck: Rage With Your Friends: A game that involves climbing a mountain with a pogo stick and competing with other players online. -
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- I Am Bread: A game that involves controlling a slice of bread and trying to become toast. It has realistic physics and a quirky story. -
- Surgeon Simulator: A game that involves performing surgery with clumsy controls and hilarious outcomes. It has various scenarios and modes to play. -
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- Email: info@zuuks.com -
- Website: https://www.zuuks.com/ -
- Facebook: https://www.facebook.com/zuuks.games -
- Instagram: https://www.instagram.com/zuuks_games/ -
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- Una gran variedad de animales: Puedes elegir entre más de 100 animales, incluyendo leones, tigres, elefantes, dinosaurios, dragones, tiburones y más. Cada animal tiene sus propias estadísticas, habilidades y comportamientos. -
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- ¿Es seguro usar Animal Revolt Battle Simulator Mod APK? -
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Diogenes is the name of the man who is stuck in a cauldron in Getting Over It. He is named after an ancient Greek philosopher who was known for living in a barrel and rejecting conventional values and norms. He was also known for his wit and his cynicism.
-Bennett Foddy is the developer and the narrator of Getting Over It. He is an independent game designer and an academic who is known for creating experimental and challenging games such as QWOP, GIRP, and CLOP. He is also a professor at New York University's Game Center and a musician who plays bass guitar for the band Cut Copy.
-Ocean of Games is a website that offers a variety of PC games for download. It has a large collection of games from different genres and categories, as well as an easy-to-use interface and a fast download speed. However, it also has some disadvantages such as being unauthorized, unsafe, illegal, and unethical.
-The answer to this question depends on your skill level, your luck, and your persistence. Some players can beat Getting Over It in less than 2 minutes, while others can take more than 200 hours. The average time to beat Getting Over It according to HowLongToBeat.com is 6 hours for the main story and 11 hours for completionists.
-If you are looking for some other games that are similar to Getting Over It in terms of concept, difficulty, or humor, then you might want to check out some of these games:
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/ TikTok .md b/spaces/1phancelerku/anime-remove-background/ TikTok .md deleted file mode 100644 index d2bcd907d9fd971fa4e535962417e5fa7d49672e..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/ TikTok .md +++ /dev/null @@ -1,101 +0,0 @@ -
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How to Download TikTok Videos Without Watermark
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Bus Simulator Ultimate 1.5.2 Mod Apk - Drive Realistic Buses with Amazing Features.md b/spaces/1phancelerku/anime-remove-background/Bus Simulator Ultimate 1.5.2 Mod Apk - Drive Realistic Buses with Amazing Features.md deleted file mode 100644 index 49f4305791c537cefe9752752822b73c03d4dd49..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Bus Simulator Ultimate 1.5.2 Mod Apk - Drive Realistic Buses with Amazing Features.md +++ /dev/null @@ -1,119 +0,0 @@ -
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Bus Simulator Ultimate 1.5.2 Mod Apk: A Review
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-What is Bus Simulator Ultimate?
-Bus Simulator Ultimate is a simulation game developed by Zuuks Games, a Turkish game studio that specializes in creating realistic driving games. The game was released in August 2019 and has since gained over 100 million downloads and 4.3 stars rating on Google Play Store.
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Features of Bus Simulator Ultimate
-Bus Simulator Ultimate has many features that make it stand out from other bus simulator games. Here are some of them:
-Realistic bus driving experience
-The game boasts realistic graphics, physics, sounds, and weather effects that make you feel like you are really driving a bus on the road. You can also choose from different camera angles, such as cockpit view, third-person view, or top-down view.
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-Customizable buses and routes
-The game offers a variety of buses that you can drive and customize. You can choose from different models, brands, sizes, colors, and designs of buses. You can also add different accessories and decorations to your buses, such as skins, stickers, horns, lights, and more. You can also change the interior of your buses, such as the seats, steering wheel, dashboard, and more.
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-The game also simulates the interaction between you and your passengers. You have to pick up and drop off passengers at the designated stops. You have to provide them with a comfortable and safe ride. You have to listen to their feedback and requests.
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How to download and install the mod apk
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Pros and cons of Bus Simulator Ultimate 1.5.2 Mod Apk
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- | Pros | Cons | | --- | --- | | Unlimited money and free purchases | May not be compatible with some devices or versions | | No ads | May cause bugs or glitches in the game | | No root | May violate the terms and conditions of the game | | Enhanced gameplay | May affect the online features of the game |Conclusion
-Bus Simulator Ultimate is a fun and realistic bus simulator game that lets you drive various buses across different countries and cities, create your own routes and destinations, run your own bus company, and compete with other players online. It has many features that make it one of the best bus simulator games for Android devices.
-Bus Simulator Ultimate 1.5.2 mod apk is a modified version of the original game that offers unlimited money, free purchases, no ads, and no root. It can enhance your gameplay experience by giving you more freedom and options in the game. However, it also has some drawbacks that you should be aware of before using it.
-If you are looking for a bus simulator game that is realistic, challenging, and entertaining, you should give Bus Simulator Ultimate a try. And if you want to get some extra benefits and features in the game, you can download and install Bus Simulator Ultimate 1.5.2 mod apk from a reliable source on the internet.
-FAQs
-Q1: Is Bus Simulator Ultimate free to play?
-A1: Yes, Bus Simulator Ultimate is free to download and play on Android devices. However, it also contains some in-app purchases that require real money.
-Q2: Is Bus Simulator Ultimate 1.5.2 Mod Apk safe to use?
-A2: Bus Simulator Ultimate 1.5.2 mod apk is generally safe to use if you download it from a trusted source on the internet. However, you should always be careful when installing apps from unknown sources as they may contain viruses or malware that can harm your device or data.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Dr Driving 3 Mod APK and Learn Driving with Fun.md b/spaces/1phancelerku/anime-remove-background/Download Dr Driving 3 Mod APK and Learn Driving with Fun.md deleted file mode 100644 index b2a60a903e5a7d8549773eef3b4a07cac23a303b..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Dr Driving 3 Mod APK and Learn Driving with Fun.md +++ /dev/null @@ -1,98 +0,0 @@ - -
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-Next, you have to download the Dr Driving 3 Mod APK file from a trusted source. There are many websites that offer modded apps, but not all of them are safe and reliable. Some of them may contain viruses or malware that can harm your device or steal your data. Therefore, you have to be careful when choosing a source to download the mod file. You can use this link to download the Dr Driving 3 Mod APK file safely and securely.
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| Is Dr Driving 3 free to play? | -Yes, Dr Driving 3 is free to play, but it has in-app purchases and ads that can affect your gameplay. | -
| Is Dr Driving 3 Mod APK safe to use? | -Yes, Dr Driving 3 Mod APK is safe to use, as long as you download it from a trusted source. However, you should always be careful when installing modded apps on your device and scan them for viruses or malware before installing them. | -
| Does Dr Driving 3 Mod APK work on all devices? | -Dr Driving 3 Mod APK works on most Android devices that support the original game. However, some devices may not be compatible with the mod or may experience some issues or errors. If you encounter any problems, you can try reinstalling the mod or contacting the mod developer for help. | -
| Can I play Dr Driving 3 offline? | -Yes, you can play Dr Driving 3 offline, but you will not be able to access the online multiplayer feature or the leaderboards. You will also not be able to sync your progress or achievements with your Google Play account. | -
| Can I update Dr Driving 3 Mod APK? | -No, you cannot update Dr Driving 3 Mod APK from the Google Play Store, as it is a modified version of the game. If you want to update the mod, you have to download and install the latest version of the mod from the same source you downloaded it from. | -
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Monster Life Mod APK Free Shopping: A Guide for Monster Lovers
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| Experience a fun and engaging gameplay with diverse monsters and activities: Monster Life has a lot of things to offer to keep you entertained and hooked. You can collect, breed, train, and battle with over 100 different monsters, each with their own abilities and personalities. You can also explore the islands of Numa and discover new places, quests, and secrets. You can also build and decorate your own island with various facilities and items. You can also interact with other players online and compete with them in battles and tournaments. | -The mod apk file may contain viruses or malware that can harm your device or data: Monster Life Mod APK Free Shopping is not an official version of the game, which means that it may not be safe or secure to download or install. The mod apk file may contain viruses or malware that can infect your device or data, or steal your personal information. You should always scan the mod apk file with an antivirus software before installing it, and backup your data before playing it. | -
| Share your monster collection and achievements with your friends on social media: Monster Life allows you to connect your game account with your Facebook account, which enables you to share your monster collection and achievements with your friends on social media. You can also invite your friends to play the game with you, or send them gifts and messages. You can also see your friends' islands and monsters, and compare your progress and rankings with them. | -The mod apk file may violate the terms of service of the original game and result in a ban or suspension: Monster Life Mod APK Free Shopping is an unauthorized version of the game, which means that it may violate the terms of service of the original game. The terms of service prohibit modifying, hacking, cheating, or exploiting the game in any way. If you play the mod apk file, you may risk getting banned or suspended from the original game, or losing your account or data. | -
Conclusion: Is Monster Life Mod APK Free Shopping worth it?
-Monster Life Mod APK Free Shopping is a fun and addictive game for anyone who loves cute monsters. It has a lot of features that make it more enjoyable and convenient to play than the original game. However, it also has some risks and drawbacks that you should be aware of before playing it. Ultimately, the decision is up to you whether you want to try it or not.
-If you decide to play Monster Life Mod APK Free Shopping, we hope that this article has helped you understand how to download and install it, how to play it, what are its features, pros and cons, and whether it is worth it or not. We hope that you have a great time playing Monster Life Mod APK Free Shopping!
-FAQs
-Here are some frequently asked questions about Monster Life Mod APK Free Shopping:
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Monster Life is a popular game by Gameloft that lets you collect, breed, train, and battle with cute monsters in a magical world called Numa.
-Monster Life Mod APK Free Shopping is a modified version of Monster Life that gives you unlimited coins and gems to buy anything you want in the game, as well as access to all the monsters in the game.
-Monster Life Mod APK Free Shopping is not an official version of the game, which means that it may not be safe or legal to download or play. The mod apk file may contain viruses or malware that can harm your device or data, or it may violate the terms of service of the original game and result in a ban or suspension. You should always scan the mod apk file with an antivirus software before installing it, and backup your data before playing it. You should also play the mod apk file at your own risk and responsibility.
-If you don't want to use Monster Life Mod APK Free Shopping, you can still get more coins and gems in Monster Life by completing quests, winning battles, watching ads, or spending real money. You can also get more coins and gems by inviting your friends to play the game with you, or by participating in events and promotions.
-To breed new monsters in Monster Life, you need to have two monsters of the same species and opposite genders. You also need to have a breeding habitat that matches their element. You can then tap on the breeding habitat and select the two monsters that you want to breed. You will then have to wait for some time until the breeding is done. You can speed up the process by using gems or watching ads. You can then hatch the egg and get a new monster.
-If you have any questions, feedback, or issues about Monster Life, you can contact the developer of the game by visiting their website [here], or by sending them an email at [support@gameloft.com]. You can also follow them on their social media accounts on [Facebook], [Twitter], [Instagram], and [YouTube].
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Extreme car driving simulator apk Free download and play the most realistic car game ever.md b/spaces/1phancelerku/anime-remove-background/Extreme car driving simulator apk Free download and play the most realistic car game ever.md deleted file mode 100644 index 05396735d762eee842f04e08c287be6589542083..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Extreme car driving simulator apk Free download and play the most realistic car game ever.md +++ /dev/null @@ -1,99 +0,0 @@ - -
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-The next step is to enable unknown sources on your device. This is a security setting that allows you to install apps from sources other than Google Play. To do this, you have to go to your device settings and look for the option that says "Unknown sources" or "Install unknown apps". Depending on your device model and Android version, this option may be located in different places, such as Security, Privacy, or Applications. You have to enable this option by tapping on it and confirming your choice.
-Install the APK file and launch the game
-The final step is to install the APK file and launch the game. To do this, you have to locate the APK file that you downloaded in your device storage, usually in the Downloads folder. You have to tap on the file and follow the instructions on the screen to install it. Once the installation is complete, you can launch the game by tapping on its icon on your home screen or app drawer. You can now enjoy playing Extreme Car Driving Simulator on your Android device.
-Conclusion
-Extreme Car Driving Simulator is a great game for car enthusiasts who want to experience driving a sports car in an open world environment. It has many features that make it realistic, fun, and challenging. You can download and install it on your Android device by following the steps we explained in this article. We hope you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below.
-FAQs
-Here are some frequently asked questions about Extreme Car Driving Simulator:
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-
Yes, Extreme Car Driving Simulator is free to download and play. However, it contains ads and in-app purchases that you can disable or buy if you want.
-Yes, Extreme Car Driving Simulator can be played offline without an internet connection. However, some features may require an internet connection, such as updating the game or accessing online leaderboards.
-Yes, Extreme Car Driving Simulator is safe to play as long as you download it from a trusted source like APKPure. You should also scan the APK file with an antivirus app before installing it.
-You can update Extreme Car Driving Simulator by downloading the latest version of the APK file from APKPure or other trusted sources. You can also check for updates within the game by tapping on the settings icon and selecting "Check for updates".
-You can get more coins in Extreme Car Driving Simulator by completing mini game checkpoint mode, driving with traffic, performing stunts, or watching ads. You can also buy coins with real money through in-app purchases.
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-
\ No newline at end of file diff --git a/spaces/AIConsultant/MusicGen/tests/modules/test_seanet.py b/spaces/AIConsultant/MusicGen/tests/modules/test_seanet.py deleted file mode 100644 index e5c51b340a2f94fb2828b14daf83d5fad645073d..0000000000000000000000000000000000000000 --- a/spaces/AIConsultant/MusicGen/tests/modules/test_seanet.py +++ /dev/null @@ -1,115 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from itertools import product - -import pytest -import torch - -from audiocraft.modules.seanet import SEANetEncoder, SEANetDecoder, SEANetResnetBlock -from audiocraft.modules import StreamableConv1d, StreamableConvTranspose1d - - -class TestSEANetModel: - - def test_base(self): - encoder = SEANetEncoder() - decoder = SEANetDecoder() - - x = torch.randn(1, 1, 24000) - z = encoder(x) - assert list(z.shape) == [1, 128, 75], z.shape - y = decoder(z) - assert y.shape == x.shape, (x.shape, y.shape) - - def test_causal(self): - encoder = SEANetEncoder(causal=True) - decoder = SEANetDecoder(causal=True) - x = torch.randn(1, 1, 24000) - - z = encoder(x) - assert list(z.shape) == [1, 128, 75], z.shape - y = decoder(z) - assert y.shape == x.shape, (x.shape, y.shape) - - def test_conv_skip_connection(self): - encoder = SEANetEncoder(true_skip=False) - decoder = SEANetDecoder(true_skip=False) - - x = torch.randn(1, 1, 24000) - z = encoder(x) - assert list(z.shape) == [1, 128, 75], z.shape - y = decoder(z) - assert y.shape == x.shape, (x.shape, y.shape) - - def test_seanet_encoder_decoder_final_act(self): - encoder = SEANetEncoder(true_skip=False) - decoder = SEANetDecoder(true_skip=False, final_activation='Tanh') - - x = torch.randn(1, 1, 24000) - z = encoder(x) - assert list(z.shape) == [1, 128, 75], z.shape - y = decoder(z) - assert y.shape == x.shape, (x.shape, y.shape) - - def _check_encoder_blocks_norm(self, encoder: SEANetEncoder, n_disable_blocks: int, norm: str): - n_blocks = 0 - for layer in encoder.model: - if isinstance(layer, StreamableConv1d): - n_blocks += 1 - assert layer.conv.norm_type == 'none' if n_blocks <= n_disable_blocks else norm - elif isinstance(layer, SEANetResnetBlock): - for resnet_layer in layer.block: - if isinstance(resnet_layer, StreamableConv1d): - # here we add + 1 to n_blocks as we increment n_blocks just after the block - assert resnet_layer.conv.norm_type == 'none' if (n_blocks + 1) <= n_disable_blocks else norm - - def test_encoder_disable_norm(self): - n_residuals = [0, 1, 3] - disable_blocks = [0, 1, 2, 3, 4, 5, 6] - norms = ['weight_norm', 'none'] - for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms): - encoder = SEANetEncoder(n_residual_layers=n_res, norm=norm, - disable_norm_outer_blocks=disable_blocks) - self._check_encoder_blocks_norm(encoder, disable_blocks, norm) - - def _check_decoder_blocks_norm(self, decoder: SEANetDecoder, n_disable_blocks: int, norm: str): - n_blocks = 0 - for layer in decoder.model: - if isinstance(layer, StreamableConv1d): - n_blocks += 1 - assert layer.conv.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm - elif isinstance(layer, StreamableConvTranspose1d): - n_blocks += 1 - assert layer.convtr.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm - elif isinstance(layer, SEANetResnetBlock): - for resnet_layer in layer.block: - if isinstance(resnet_layer, StreamableConv1d): - assert resnet_layer.conv.norm_type == 'none' \ - if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm - - def test_decoder_disable_norm(self): - n_residuals = [0, 1, 3] - disable_blocks = [0, 1, 2, 3, 4, 5, 6] - norms = ['weight_norm', 'none'] - for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms): - decoder = SEANetDecoder(n_residual_layers=n_res, norm=norm, - disable_norm_outer_blocks=disable_blocks) - self._check_decoder_blocks_norm(decoder, disable_blocks, norm) - - def test_disable_norm_raises_exception(self): - # Invalid disable_norm_outer_blocks values raise exceptions - with pytest.raises(AssertionError): - SEANetEncoder(disable_norm_outer_blocks=-1) - - with pytest.raises(AssertionError): - SEANetEncoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7) - - with pytest.raises(AssertionError): - SEANetDecoder(disable_norm_outer_blocks=-1) - - with pytest.raises(AssertionError): - SEANetDecoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7) diff --git a/spaces/AIGC-Audio/Make_An_Audio/ldm/models/diffusion/classifier.py b/spaces/AIGC-Audio/Make_An_Audio/ldm/models/diffusion/classifier.py deleted file mode 100644 index 67e98b9d8ffb96a150b517497ace0a242d7163ef..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/Make_An_Audio/ldm/models/diffusion/classifier.py +++ /dev/null @@ -1,267 +0,0 @@ -import os -import torch -import pytorch_lightning as pl -from omegaconf import OmegaConf -from torch.nn import functional as F -from torch.optim import AdamW -from torch.optim.lr_scheduler import LambdaLR -from copy import deepcopy -from einops import rearrange -from glob import glob -from natsort import natsorted - -from ldm.modules.diffusionmodules.openaimodel import EncoderUNetModel, UNetModel -from ldm.util import log_txt_as_img, default, ismap, instantiate_from_config - -__models__ = { - 'class_label': EncoderUNetModel, - 'segmentation': UNetModel -} - - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -class NoisyLatentImageClassifier(pl.LightningModule): - - def __init__(self, - diffusion_path, - num_classes, - ckpt_path=None, - pool='attention', - label_key=None, - diffusion_ckpt_path=None, - scheduler_config=None, - weight_decay=1.e-2, - log_steps=10, - monitor='val/loss', - *args, - **kwargs): - super().__init__(*args, **kwargs) - self.num_classes = num_classes - # get latest config of diffusion model - diffusion_config = natsorted(glob(os.path.join(diffusion_path, 'configs', '*-project.yaml')))[-1] - self.diffusion_config = OmegaConf.load(diffusion_config).model - self.diffusion_config.params.ckpt_path = diffusion_ckpt_path - self.load_diffusion() - - self.monitor = monitor - self.numd = self.diffusion_model.first_stage_model.encoder.num_resolutions - 1 - self.log_time_interval = self.diffusion_model.num_timesteps // log_steps - self.log_steps = log_steps - - self.label_key = label_key if not hasattr(self.diffusion_model, 'cond_stage_key') \ - else self.diffusion_model.cond_stage_key - - assert self.label_key is not None, 'label_key neither in diffusion model nor in model.params' - - if self.label_key not in __models__: - raise NotImplementedError() - - self.load_classifier(ckpt_path, pool) - - self.scheduler_config = scheduler_config - self.use_scheduler = self.scheduler_config is not None - self.weight_decay = weight_decay - - def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): - sd = torch.load(path, map_location="cpu") - if "state_dict" in list(sd.keys()): - sd = sd["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( - sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: - print(f"Missing Keys: {missing}") - if len(unexpected) > 0: - print(f"Unexpected Keys: {unexpected}") - - def load_diffusion(self): - model = instantiate_from_config(self.diffusion_config) - self.diffusion_model = model.eval() - self.diffusion_model.train = disabled_train - for param in self.diffusion_model.parameters(): - param.requires_grad = False - - def load_classifier(self, ckpt_path, pool): - model_config = deepcopy(self.diffusion_config.params.unet_config.params) - model_config.in_channels = self.diffusion_config.params.unet_config.params.out_channels - model_config.out_channels = self.num_classes - if self.label_key == 'class_label': - model_config.pool = pool - - self.model = __models__[self.label_key](**model_config) - if ckpt_path is not None: - print('#####################################################################') - print(f'load from ckpt "{ckpt_path}"') - print('#####################################################################') - self.init_from_ckpt(ckpt_path) - - @torch.no_grad() - def get_x_noisy(self, x, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x)) - continuous_sqrt_alpha_cumprod = None - if self.diffusion_model.use_continuous_noise: - continuous_sqrt_alpha_cumprod = self.diffusion_model.sample_continuous_noise_level(x.shape[0], t + 1) - # todo: make sure t+1 is correct here - - return self.diffusion_model.q_sample(x_start=x, t=t, noise=noise, - continuous_sqrt_alpha_cumprod=continuous_sqrt_alpha_cumprod) - - def forward(self, x_noisy, t, *args, **kwargs): - return self.model(x_noisy, t) - - @torch.no_grad() - def get_input(self, batch, k): - x = batch[k] - if len(x.shape) == 3: - x = x[..., None] - x = rearrange(x, 'b h w c -> b c h w') - x = x.to(memory_format=torch.contiguous_format).float() - return x - - @torch.no_grad() - def get_conditioning(self, batch, k=None): - if k is None: - k = self.label_key - assert k is not None, 'Needs to provide label key' - - targets = batch[k].to(self.device) - - if self.label_key == 'segmentation': - targets = rearrange(targets, 'b h w c -> b c h w') - for down in range(self.numd): - h, w = targets.shape[-2:] - targets = F.interpolate(targets, size=(h // 2, w // 2), mode='nearest') - - # targets = rearrange(targets,'b c h w -> b h w c') - - return targets - - def compute_top_k(self, logits, labels, k, reduction="mean"): - _, top_ks = torch.topk(logits, k, dim=1) - if reduction == "mean": - return (top_ks == labels[:, None]).float().sum(dim=-1).mean().item() - elif reduction == "none": - return (top_ks == labels[:, None]).float().sum(dim=-1) - - def on_train_epoch_start(self): - # save some memory - self.diffusion_model.model.to('cpu') - - @torch.no_grad() - def write_logs(self, loss, logits, targets): - log_prefix = 'train' if self.training else 'val' - log = {} - log[f"{log_prefix}/loss"] = loss.mean() - log[f"{log_prefix}/acc@1"] = self.compute_top_k( - logits, targets, k=1, reduction="mean" - ) - log[f"{log_prefix}/acc@5"] = self.compute_top_k( - logits, targets, k=5, reduction="mean" - ) - - self.log_dict(log, prog_bar=False, logger=True, on_step=self.training, on_epoch=True) - self.log('loss', log[f"{log_prefix}/loss"], prog_bar=True, logger=False) - self.log('global_step', self.global_step, logger=False, on_epoch=False, prog_bar=True) - lr = self.optimizers().param_groups[0]['lr'] - self.log('lr_abs', lr, on_step=True, logger=True, on_epoch=False, prog_bar=True) - - def shared_step(self, batch, t=None): - x, *_ = self.diffusion_model.get_input(batch, k=self.diffusion_model.first_stage_key) - targets = self.get_conditioning(batch) - if targets.dim() == 4: - targets = targets.argmax(dim=1) - if t is None: - t = torch.randint(0, self.diffusion_model.num_timesteps, (x.shape[0],), device=self.device).long() - else: - t = torch.full(size=(x.shape[0],), fill_value=t, device=self.device).long() - x_noisy = self.get_x_noisy(x, t) - logits = self(x_noisy, t) - - loss = F.cross_entropy(logits, targets, reduction='none') - - self.write_logs(loss.detach(), logits.detach(), targets.detach()) - - loss = loss.mean() - return loss, logits, x_noisy, targets - - def training_step(self, batch, batch_idx): - loss, *_ = self.shared_step(batch) - return loss - - def reset_noise_accs(self): - self.noisy_acc = {t: {'acc@1': [], 'acc@5': []} for t in - range(0, self.diffusion_model.num_timesteps, self.diffusion_model.log_every_t)} - - def on_validation_start(self): - self.reset_noise_accs() - - @torch.no_grad() - def validation_step(self, batch, batch_idx): - loss, *_ = self.shared_step(batch) - - for t in self.noisy_acc: - _, logits, _, targets = self.shared_step(batch, t) - self.noisy_acc[t]['acc@1'].append(self.compute_top_k(logits, targets, k=1, reduction='mean')) - self.noisy_acc[t]['acc@5'].append(self.compute_top_k(logits, targets, k=5, reduction='mean')) - - return loss - - def configure_optimizers(self): - optimizer = AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) - - if self.use_scheduler: - scheduler = instantiate_from_config(self.scheduler_config) - - print("Setting up LambdaLR scheduler...") - scheduler = [ - { - 'scheduler': LambdaLR(optimizer, lr_lambda=scheduler.schedule), - 'interval': 'step', - 'frequency': 1 - }] - return [optimizer], scheduler - - return optimizer - - @torch.no_grad() - def log_images(self, batch, N=8, *args, **kwargs): - log = dict() - x = self.get_input(batch, self.diffusion_model.first_stage_key) - log['inputs'] = x - - y = self.get_conditioning(batch) - - if self.label_key == 'class_label': - y = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"]) - log['labels'] = y - - if ismap(y): - log['labels'] = self.diffusion_model.to_rgb(y) - - for step in range(self.log_steps): - current_time = step * self.log_time_interval - - _, logits, x_noisy, _ = self.shared_step(batch, t=current_time) - - log[f'inputs@t{current_time}'] = x_noisy - - pred = F.one_hot(logits.argmax(dim=1), num_classes=self.num_classes) - pred = rearrange(pred, 'b h w c -> b c h w') - - log[f'pred@t{current_time}'] = self.diffusion_model.to_rgb(pred) - - for key in log: - log[key] = log[key][:N] - - return log diff --git a/spaces/ALSv/FSW/roop/processors/frame/__init__.py b/spaces/ALSv/FSW/roop/processors/frame/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Aaaad/Dddde/app.py b/spaces/Aaaad/Dddde/app.py deleted file mode 100644 index f1d4beb0a8f3cee27903f527b6bf8daa485a75a0..0000000000000000000000000000000000000000 --- a/spaces/Aaaad/Dddde/app.py +++ /dev/null @@ -1,3 +0,0 @@ -import gradio as gr - -gr.Interface.load("huggingface/gpt2").launch() \ No newline at end of file diff --git a/spaces/Abhilashvj/planogram-compliance/utils/autoanchor.py b/spaces/Abhilashvj/planogram-compliance/utils/autoanchor.py deleted file mode 100644 index 1c763fc634121aac8aa6a9f99bb4a99c06b23910..0000000000000000000000000000000000000000 --- a/spaces/Abhilashvj/planogram-compliance/utils/autoanchor.py +++ /dev/null @@ -1,219 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -AutoAnchor utils -""" - -import random - -import numpy as np -import torch -import yaml -from tqdm import tqdm - -from utils import TryExcept -from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr - -PREFIX = colorstr("AutoAnchor: ") - - -def check_anchor_order(m): - # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary - a = ( - m.anchors.prod(-1).mean(-1).view(-1) - ) # mean anchor area per output layer - da = a[-1] - a[0] # delta a - ds = m.stride[-1] - m.stride[0] # delta s - if da and (da.sign() != ds.sign()): # same order - LOGGER.info(f"{PREFIX}Reversing anchor order") - m.anchors[:] = m.anchors.flip(0) - - -@TryExcept(f"{PREFIX}ERROR") -def check_anchors(dataset, model, thr=4.0, imgsz=640): - # Check anchor fit to data, recompute if necessary - m = ( - model.module.model[-1] if hasattr(model, "module") else model.model[-1] - ) # Detect() - shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) - scale = np.random.uniform( - 0.9, 1.1, size=(shapes.shape[0], 1) - ) # augment scale - wh = torch.tensor( - np.concatenate( - [l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)] - ) - ).float() # wh - - def metric(k): # compute metric - r = wh[:, None] / k[None] - x = torch.min(r, 1 / r).min(2)[0] # ratio metric - best = x.max(1)[0] # best_x - aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold - bpr = (best > 1 / thr).float().mean() # best possible recall - return bpr, aat - - stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides - anchors = m.anchors.clone() * stride # current anchors - bpr, aat = metric(anchors.cpu().view(-1, 2)) - s = f"\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). " - if bpr > 0.98: # threshold to recompute - LOGGER.info(f"{s}Current anchors are a good fit to dataset ✅") - else: - LOGGER.info( - f"{s}Anchors are a poor fit to dataset ⚠️, attempting to improve..." - ) - na = m.anchors.numel() // 2 # number of anchors - anchors = kmean_anchors( - dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False - ) - new_bpr = metric(anchors)[0] - if new_bpr > bpr: # replace anchors - anchors = torch.tensor(anchors, device=m.anchors.device).type_as( - m.anchors - ) - m.anchors[:] = anchors.clone().view_as(m.anchors) - check_anchor_order(m) # must be in pixel-space (not grid-space) - m.anchors /= stride - s = f"{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)" - else: - s = f"{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)" - LOGGER.info(s) - - -def kmean_anchors( - dataset="./data/coco128.yaml", - n=9, - img_size=640, - thr=4.0, - gen=1000, - verbose=True, -): - """Creates kmeans-evolved anchors from training dataset - - Arguments: - dataset: path to data.yaml, or a loaded dataset - n: number of anchors - img_size: image size used for training - thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 - gen: generations to evolve anchors using genetic algorithm - verbose: print all results - - Return: - k: kmeans evolved anchors - - Usage: - from utils.autoanchor import *; _ = kmean_anchors() - """ - from scipy.cluster.vq import kmeans - - npr = np.random - thr = 1 / thr - - def metric(k, wh): # compute metrics - r = wh[:, None] / k[None] - x = torch.min(r, 1 / r).min(2)[0] # ratio metric - # x = wh_iou(wh, torch.tensor(k)) # iou metric - return x, x.max(1)[0] # x, best_x - - def anchor_fitness(k): # mutation fitness - _, best = metric(torch.tensor(k, dtype=torch.float32), wh) - return (best * (best > thr).float()).mean() # fitness - - def print_results(k, verbose=True): - k = k[np.argsort(k.prod(1))] # sort small to large - x, best = metric(k, wh0) - bpr, aat = (best > thr).float().mean(), ( - x > thr - ).float().mean() * n # best possible recall, anch > thr - s = ( - f"{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n" - f"{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, " - f"past_thr={x[x > thr].mean():.3f}-mean: " - ) - for x in k: - s += "%i,%i, " % (round(x[0]), round(x[1])) - if verbose: - LOGGER.info(s[:-2]) - return k - - if isinstance(dataset, str): # *.yaml file - with open(dataset, errors="ignore") as f: - data_dict = yaml.safe_load(f) # model dict - from utils.dataloaders import LoadImagesAndLabels - - dataset = LoadImagesAndLabels( - data_dict["train"], augment=True, rect=True - ) - - # Get label wh - shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) - wh0 = np.concatenate( - [l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)] - ) # wh - - # Filter - i = (wh0 < 3.0).any(1).sum() - if i: - LOGGER.info( - f"{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size" - ) - wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels - # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 - - # Kmeans init - try: - LOGGER.info( - f"{PREFIX}Running kmeans for {n} anchors on {len(wh)} points..." - ) - assert n <= len(wh) # apply overdetermined constraint - s = wh.std(0) # sigmas for whitening - k = kmeans(wh / s, n, iter=30)[0] * s # points - assert n == len( - k - ) # kmeans may return fewer points than requested if wh is insufficient or too similar - except Exception: - LOGGER.warning( - f"{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init" - ) - k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init - wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0)) - k = print_results(k, verbose=False) - - # Plot - # k, d = [None] * 20, [None] * 20 - # for i in tqdm(range(1, 21)): - # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance - # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) - # ax = ax.ravel() - # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') - # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh - # ax[0].hist(wh[wh[:, 0]<100, 0],400) - # ax[1].hist(wh[wh[:, 1]<100, 1],400) - # fig.savefig('wh.png', dpi=200) - - # Evolve - f, sh, mp, s = ( - anchor_fitness(k), - k.shape, - 0.9, - 0.1, - ) # fitness, generations, mutation prob, sigma - pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar - for _ in pbar: - v = np.ones(sh) - while ( - v == 1 - ).all(): # mutate until a change occurs (prevent duplicates) - v = ( - (npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s - + 1 - ).clip(0.3, 3.0) - kg = (k.copy() * v).clip(min=2.0) - fg = anchor_fitness(kg) - if fg > f: - f, k = fg, kg.copy() - pbar.desc = f"{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}" - if verbose: - print_results(k, verbose) - - return print_results(k).astype(np.float32) diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/base/Base.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/base/Base.js deleted file mode 100644 index 79318136741ce98d3d8b0958b918e14ac243c493..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/base/Base.js +++ /dev/null @@ -1,112 +0,0 @@ -import BaseShapes from '../../../plugins/gameobjects/shape/shapes/BaseShapes.js'; -import EaseValueMethods from './EaseValueMethods.js'; - -const GetValue = Phaser.Utils.Objects.GetValue; - -class Base extends BaseShapes { - constructor(scene, config) { - var x = GetValue(config, 'x', 0); - var y = GetValue(config, 'y', 0); - var width = GetValue(config, 'width', 64); - var height = GetValue(config, 'height', 64); - - super(scene, x, y, width, height); - - this.setDuration(GetValue(config, 'duration', 1000)); - this.setEase(GetValue(config, 'ease', 'Linear')); - this.setDelay(GetValue(config, 'delay', 0)); - this.setRepeatDelay(GetValue(config, 'repeatDelay', 0)); - var color = GetValue(config, 'color', 0xffffff); - var start = GetValue(config, 'start', true); - - this.buildShapes(config); - this.setColor(color); - this.setValue(0); - - if (start) { - this.start(); - } - } - - buildShapes() { - - } - - get centerX() { - return this.width / 2;; - } - - get centerY() { - return this.height / 2; - } - - get radius() { - return Math.min(this.centerX, this.centerY); - } - - get color() { - return this._color; - } - - set color(value) { - this.isColorChanged = this.isColorChanged || (this._color !== value); - this.dirty = this.dirty || this.isColorChanged; - this._color = value; - this.setShapesColor(value); - } - - setColor(color) { - this.color = color; - return this; - } - - setShapesColor(color) { - - } - - get value() { - return this._value; - } - - set value(value) { - value = Phaser.Math.Clamp(value, 0, 1); - this.dirty = this.dirty || (this._value != value); - this._value = value; - } - - setValue(value) { - this.value = value; - return this; - } - - setDuration(duration) { - this.duration = duration; - return this; - } - - setDelay(delay) { - this.delay = delay; - return this; - } - - setRepeatDelay(repeatDelay) { - this.repeatDelay = repeatDelay; - return this; - } - - setEase(ease) { - this.ease = ease; - return this; - } - - get isRunning() { - return (this.tweenTask) ? this.tweenTask.isRunning : false; - } -} - -Object.assign( - Base.prototype, - EaseValueMethods -); - -export default Base; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/EaseMoveMethods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/EaseMoveMethods.js deleted file mode 100644 index 04cbc921187b2c8ce026b590031135a28d00e937..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/EaseMoveMethods.js +++ /dev/null @@ -1,120 +0,0 @@ -import { EaseMoveTo, EaseMoveFrom } from '../easemove/EaseMove.js'; -import { WaitComplete } from '../utils/WaitEvent.js'; -import GetParentSizerMethods from './GetParentSizerMethods.js'; - -const IsPlainObject = Phaser.Utils.Objects.IsPlainObject; -const DistanceBetween = Phaser.Math.Distance.Between; - -var OnInitEaseMove = function (gameObject, easeMove) { - // Route 'complete' of easeMove to gameObject - easeMove.completeEventName = undefined; - easeMove.on('complete', function () { - if (easeMove.completeEventName) { - gameObject.emit(easeMove.completeEventName, gameObject); - easeMove.completeEventName = undefined; - } - }) - - // Update local state - easeMove.on('update', function () { - var parent = GetParentSizerMethods.getParentSizer(gameObject); - if (parent) { - parent.resetChildPositionState(gameObject); - } - }) -} - -export default { - moveFrom(duration, x, y, ease, destroyMode) { - if (IsPlainObject(duration)) { - var config = duration; - x = config.x; - y = config.y; - if (config.hasOwnProperty('speed')) { - duration = (DistanceBetween(x, y, this.x, this.y) * 1000) / config.speed; - } else { - duration = config.duration; - } - - ease = config.ease; - } - - var isInit = (this._easeMove === undefined); - - this._easeMove = EaseMoveFrom(this, duration, x, y, ease, destroyMode, this._easeMove); - - if (isInit) { - OnInitEaseMove(this, this._easeMove); - } - - this._easeMove.completeEventName = 'movefrom.complete'; - - return this; - }, - - moveFromPromise(duration, x, y, ease, destroyMode) { - this.moveFrom(duration, x, y, ease, destroyMode); - return WaitComplete(this._easeMove); - }, - - moveFromDestroy(duration, x, y, ease) { - this.moveFrom(duration, x, y, ease, true); - return this; - }, - - moveFromDestroyPromise(duration, x, y, ease) { - this.moveFromDestroy(duration, x, y, ease); - return WaitComplete(this._easeMove); - }, - - moveTo(duration, x, y, ease, destroyMode) { - if (IsPlainObject(duration)) { - var config = duration; - x = config.x; - y = config.y; - if (config.hasOwnProperty('speed')) { - duration = (DistanceBetween(x, y, this.x, this.y) * 1000) / config.speed; - } else { - duration = config.duration; - } - - ease = config.ease; - } - - var isInit = (this._easeMove === undefined); - - this._easeMove = EaseMoveTo(this, duration, x, y, ease, destroyMode, this._easeMove); - - if (isInit) { - OnInitEaseMove(this, this._easeMove); - } - - this._easeMove.completeEventName = 'moveto.complete'; - - return this; - }, - - moveToPromise(duration, x, y, ease, destroyMode) { - this.moveTo(duration, x, y, ease, destroyMode); - return WaitComplete(this._easeMove); - }, - - moveToDestroy(duration, x, y, ease) { - this.moveTo(duration, x, y, ease, true) - return this; - }, - - moveToDestroyPromise(duration, x, y, ease) { - this.moveToDestroy(duration, x, y, ease, true); - return WaitComplete(this._easeMove); - }, - - moveStop(toEnd) { - if (!this._easeMove) { - return this; - } - - this._easeMove.stop(toEnd); - return this; - } -} \ No newline at end of file diff --git a/spaces/Aloento/9Nine-PITS/text/frontend/normalizer/acronyms.py b/spaces/Aloento/9Nine-PITS/text/frontend/normalizer/acronyms.py deleted file mode 100644 index abf198b97e6e818e1fbe59006f98492640bcee54..0000000000000000000000000000000000000000 --- a/spaces/Aloento/9Nine-PITS/text/frontend/normalizer/acronyms.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle 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. diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/utils/ImagesDataset.py b/spaces/Amrrs/DragGan-Inversion/PTI/utils/ImagesDataset.py deleted file mode 100644 index 4d36e8665270f4f6dee5a2d58a36c564e1543771..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/PTI/utils/ImagesDataset.py +++ /dev/null @@ -1,43 +0,0 @@ -import os - -from torch.utils.data import Dataset -from PIL import Image - -from PTI.utils.data_utils import make_dataset -from torchvision import transforms - - -class Image2Dataset(Dataset): - def __init__(self, image) -> None: - super().__init__() - self.image = image - self.transform = transforms.Compose( - [ - transforms.ToTensor(), - transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), - ] - ) - - def __len__(self): - return 1 - - def __getitem__(self, index): - return "customIMG", self.transform(self.image) - - -class ImagesDataset(Dataset): - def __init__(self, source_root, source_transform=None): - self.source_paths = sorted(make_dataset(source_root)) - self.source_transform = source_transform - - def __len__(self): - return len(self.source_paths) - - def __getitem__(self, index): - fname, from_path = self.source_paths[index] - from_im = Image.open(from_path).convert("RGB").resize([1024, 1024]) - - if self.source_transform: - from_im = self.source_transform(from_im) - - return fname, from_im diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/openpose/src/__init__.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/openpose/src/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/other-formats.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/other-formats.md deleted file mode 100644 index b58d00fce180e4cd2a069a970300ed173c867be3..0000000000000000000000000000000000000000 --- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/other-formats.md +++ /dev/null @@ -1,194 +0,0 @@ - - -# Load different Stable Diffusion formats - -[[open-in-colab]] - -Stable Diffusion models are available in different formats depending on the framework they're trained and saved with, and where you download them from. Converting these formats for use in 🤗 Diffusers allows you to use all the features supported by the library, such as [using different schedulers](schedulers) for inference, [building your custom pipeline](write_own_pipeline), and a variety of techniques and methods for [optimizing inference speed](./optimization/opt_overview). - -
- 
-
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py
deleted file mode 100644
index 1b695f0e19049dc91b7656d7684df151896b7727..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py
+++ /dev/null
@@ -1,12 +0,0 @@
-_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
-model = dict(
- roi_head=dict(
- bbox_roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(
- _delete_=True,
- type='DeformRoIPoolPack',
- output_size=7,
- output_channels=256),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32])))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py
deleted file mode 100644
index e15bc29b03d8c612a8921873d456a03126f79aae..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py
+++ /dev/null
@@ -1,63 +0,0 @@
-_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
-model = dict(
- pretrained='open-mmlab://detectron2/resnet50_caffe',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=False),
- norm_eval=True,
- style='caffe'),
- roi_head=dict(
- bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
- # model training and testing settings
- train_cfg=dict(
- rcnn=dict(
- assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
- sampler=dict(num=256))),
- test_cfg=dict(rcnn=dict(score_thr=1e-3)))
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
- mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadProposals', num_max_proposals=300),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadProposals', num_max_proposals=None),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img', 'proposals']),
- ])
-]
-data = dict(
- train=dict(
- proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_train2017.pkl',
- pipeline=train_pipeline),
- val=dict(
- proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl',
- pipeline=test_pipeline),
- test=dict(
- proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl',
- pipeline=test_pipeline))
-optimizer_config = dict(
- _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py b/spaces/Andy1621/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
deleted file mode 100644
index 0cef0f09bfa2290d14fc3a783ea500d6c3da2931..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
+++ /dev/null
@@ -1,7 +0,0 @@
-_base_ = [
- '../_base_/models/danet_r50-d8.py',
- '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
- decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21))
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/deprecated_wrappers.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/deprecated_wrappers.py
deleted file mode 100644
index a2e593df9ee57637038683d7a1efaa347b2b69e7..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/deprecated_wrappers.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# This file is for backward compatibility.
-# Module wrappers for empty tensor have been moved to mmcv.cnn.bricks.
-import warnings
-
-from ..cnn.bricks.wrappers import Conv2d, ConvTranspose2d, Linear, MaxPool2d
-
-
-class Conv2d_deprecated(Conv2d):
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- warnings.warn(
- 'Importing Conv2d wrapper from "mmcv.ops" will be deprecated in'
- ' the future. Please import them from "mmcv.cnn" instead')
-
-
-class ConvTranspose2d_deprecated(ConvTranspose2d):
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- warnings.warn(
- 'Importing ConvTranspose2d wrapper from "mmcv.ops" will be '
- 'deprecated in the future. Please import them from "mmcv.cnn" '
- 'instead')
-
-
-class MaxPool2d_deprecated(MaxPool2d):
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- warnings.warn(
- 'Importing MaxPool2d wrapper from "mmcv.ops" will be deprecated in'
- ' the future. Please import them from "mmcv.cnn" instead')
-
-
-class Linear_deprecated(Linear):
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- warnings.warn(
- 'Importing Linear wrapper from "mmcv.ops" will be deprecated in'
- ' the future. Please import them from "mmcv.cnn" instead')
diff --git a/spaces/Artbogdanov/monet-manet/README.md b/spaces/Artbogdanov/monet-manet/README.md
deleted file mode 100644
index 41d6eca1c61c55241eab63384d932929507de874..0000000000000000000000000000000000000000
--- a/spaces/Artbogdanov/monet-manet/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Fast Ai Pics
-emoji: 🌖
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/demo/inference_on_a_image.py b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/demo/inference_on_a_image.py
deleted file mode 100644
index 0dd332f36725d96351156482959387b6124f3f5f..0000000000000000000000000000000000000000
--- a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/demo/inference_on_a_image.py
+++ /dev/null
@@ -1,214 +0,0 @@
-import argparse
-import os
-import sys
-
-import numpy as np
-import torch
-from PIL import Image, ImageDraw, ImageFont
-
-import groundingdino.datasets.transforms as T
-from groundingdino.models import build_model
-from groundingdino.util import box_ops
-from groundingdino.util.slconfig import SLConfig
-from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
-from groundingdino.util.vl_utils import create_positive_map_from_span
-
-
-def plot_boxes_to_image(image_pil, tgt):
- H, W = tgt["size"]
- boxes = tgt["boxes"]
- labels = tgt["labels"]
- assert len(boxes) == len(labels), "boxes and labels must have same length"
-
- draw = ImageDraw.Draw(image_pil)
- mask = Image.new("L", image_pil.size, 0)
- mask_draw = ImageDraw.Draw(mask)
-
- # draw boxes and masks
- for box, label in zip(boxes, labels):
- # from 0..1 to 0..W, 0..H
- box = box * torch.Tensor([W, H, W, H])
- # from xywh to xyxy
- box[:2] -= box[2:] / 2
- box[2:] += box[:2]
- # random color
- color = tuple(np.random.randint(0, 255, size=3).tolist())
- # draw
- x0, y0, x1, y1 = box
- x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)
-
- draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
- # draw.text((x0, y0), str(label), fill=color)
-
- font = ImageFont.load_default()
- if hasattr(font, "getbbox"):
- bbox = draw.textbbox((x0, y0), str(label), font)
- else:
- w, h = draw.textsize(str(label), font)
- bbox = (x0, y0, w + x0, y0 + h)
- # bbox = draw.textbbox((x0, y0), str(label))
- draw.rectangle(bbox, fill=color)
- draw.text((x0, y0), str(label), fill="white")
-
- mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=6)
-
- return image_pil, mask
-
-
-def load_image(image_path):
- # load image
- image_pil = Image.open(image_path).convert("RGB") # load image
-
- transform = T.Compose(
- [
- T.RandomResize([800], max_size=1333),
- T.ToTensor(),
- T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
- ]
- )
- image, _ = transform(image_pil, None) # 3, h, w
- return image_pil, image
-
-
-def load_model(model_config_path, model_checkpoint_path, cpu_only=False):
- args = SLConfig.fromfile(model_config_path)
- args.device = "cuda" if not cpu_only else "cpu"
- model = build_model(args)
- checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
- load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
- print(load_res)
- _ = model.eval()
- return model
-
-
-def get_grounding_output(model, image, caption, box_threshold, text_threshold=None, with_logits=True, cpu_only=False, token_spans=None):
- assert text_threshold is not None or token_spans is not None, "text_threshould and token_spans should not be None at the same time!"
- caption = caption.lower()
- caption = caption.strip()
- if not caption.endswith("."):
- caption = caption + "."
- device = "cuda" if not cpu_only else "cpu"
- model = model.to(device)
- image = image.to(device)
- with torch.no_grad():
- outputs = model(image[None], captions=[caption])
- logits = outputs["pred_logits"].sigmoid()[0] # (nq, 256)
- boxes = outputs["pred_boxes"][0] # (nq, 4)
-
- # filter output
- if token_spans is None:
- logits_filt = logits.cpu().clone()
- boxes_filt = boxes.cpu().clone()
- filt_mask = logits_filt.max(dim=1)[0] > box_threshold
- logits_filt = logits_filt[filt_mask] # num_filt, 256
- boxes_filt = boxes_filt[filt_mask] # num_filt, 4
-
- # get phrase
- tokenlizer = model.tokenizer
- tokenized = tokenlizer(caption)
- # build pred
- pred_phrases = []
- for logit, box in zip(logits_filt, boxes_filt):
- pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)
- if with_logits:
- pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
- else:
- pred_phrases.append(pred_phrase)
- else:
- # given-phrase mode
- positive_maps = create_positive_map_from_span(
- model.tokenizer(text_prompt),
- token_span=token_spans
- ).to(image.device) # n_phrase, 256
-
- logits_for_phrases = positive_maps @ logits.T # n_phrase, nq
- all_logits = []
- all_phrases = []
- all_boxes = []
- for (token_span, logit_phr) in zip(token_spans, logits_for_phrases):
- # get phrase
- phrase = ' '.join([caption[_s:_e] for (_s, _e) in token_span])
- # get mask
- filt_mask = logit_phr > box_threshold
- # filt box
- all_boxes.append(boxes[filt_mask])
- # filt logits
- all_logits.append(logit_phr[filt_mask])
- if with_logits:
- logit_phr_num = logit_phr[filt_mask]
- all_phrases.extend([phrase + f"({str(logit.item())[:4]})" for logit in logit_phr_num])
- else:
- all_phrases.extend([phrase for _ in range(len(filt_mask))])
- boxes_filt = torch.cat(all_boxes, dim=0).cpu()
- pred_phrases = all_phrases
-
-
- return boxes_filt, pred_phrases
-
-
-if __name__ == "__main__":
-
- parser = argparse.ArgumentParser("Grounding DINO example", add_help=True)
- parser.add_argument("--config_file", "-c", type=str, required=True, help="path to config file")
- parser.add_argument(
- "--checkpoint_path", "-p", type=str, required=True, help="path to checkpoint file"
- )
- parser.add_argument("--image_path", "-i", type=str, required=True, help="path to image file")
- parser.add_argument("--text_prompt", "-t", type=str, required=True, help="text prompt")
- parser.add_argument(
- "--output_dir", "-o", type=str, default="outputs", required=True, help="output directory"
- )
-
- parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold")
- parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold")
- parser.add_argument("--token_spans", type=str, default=None, help=
- "The positions of start and end positions of phrases of interest. \
- For example, a caption is 'a cat and a dog', \
- if you would like to detect 'cat', the token_spans should be '[[[2, 5]], ]', since 'a cat and a dog'[2:5] is 'cat'. \
- if you would like to detect 'a cat', the token_spans should be '[[[0, 1], [2, 5]], ]', since 'a cat and a dog'[0:1] is 'a', and 'a cat and a dog'[2:5] is 'cat'. \
- ")
-
- parser.add_argument("--cpu-only", action="store_true", help="running on cpu only!, default=False")
- args = parser.parse_args()
-
- # cfg
- config_file = args.config_file # change the path of the model config file
- checkpoint_path = args.checkpoint_path # change the path of the model
- image_path = args.image_path
- text_prompt = args.text_prompt
- output_dir = args.output_dir
- box_threshold = args.box_threshold
- text_threshold = args.text_threshold
- token_spans = args.token_spans
-
- # make dir
- os.makedirs(output_dir, exist_ok=True)
- # load image
- image_pil, image = load_image(image_path)
- # load model
- model = load_model(config_file, checkpoint_path, cpu_only=args.cpu_only)
-
- # visualize raw image
- image_pil.save(os.path.join(output_dir, "raw_image.jpg"))
-
- # set the text_threshold to None if token_spans is set.
- if token_spans is not None:
- text_threshold = None
- print("Using token_spans. Set the text_threshold to None.")
-
-
- # run model
- boxes_filt, pred_phrases = get_grounding_output(
- model, image, text_prompt, box_threshold, text_threshold, cpu_only=args.cpu_only, token_spans=eval(token_spans)
- )
-
- # visualize pred
- size = image_pil.size
- pred_dict = {
- "boxes": boxes_filt,
- "size": [size[1], size[0]], # H,W
- "labels": pred_phrases,
- }
- # import ipdb; ipdb.set_trace()
- image_with_box = plot_boxes_to_image(image_pil, pred_dict)[0]
- image_with_box.save(os.path.join(output_dir, "pred.jpg"))
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/__init__.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/BalaBhaskarudu/mygenAIChatbot/app.py b/spaces/BalaBhaskarudu/mygenAIChatbot/app.py
deleted file mode 100644
index 2dbf3ae89c2e3fdab7134107dd346f984dca8eb1..0000000000000000000000000000000000000000
--- a/spaces/BalaBhaskarudu/mygenAIChatbot/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-def get_text_response(user_message,history):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/Benson/text-generation/Examples/Animal Rebelin Batalla Simulador Mod Apk Desbloqueado Todo.md b/spaces/Benson/text-generation/Examples/Animal Rebelin Batalla Simulador Mod Apk Desbloqueado Todo.md
deleted file mode 100644
index bedcf49684a04991a2fa4e12b88c0f420413dfb1..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Animal Rebelin Batalla Simulador Mod Apk Desbloqueado Todo.md
+++ /dev/null
@@ -1,61 +0,0 @@
-
-
-Simulador de batalla de rebelión animal Mod APK desbloqueado todo
-Si eres un fan de las batallas épicas y las simulaciones de animales, es posible que quieras echar un vistazo a Animal Revolt Battle Simulator, un juego que te permite crear y ver peleas de animales realistas en 3D. Y si quieres disfrutar del juego con dinero ilimitado, menú, y todas las características desbloqueadas, es posible que desee descargar Animal Revolt Battle Simulator Mod APK, una versión modificada del juego original que le da más libertad y diversión. En este artículo, te contaremos todo lo que necesitas saber sobre Animal Revolt Battle Simulator y su versión modificada.
-¿Qué es el simulador de batalla de rebelión animal?
-Animal Revolt Battle Simulator es un juego desarrollado por Beast Battle Games, un estudio especializado en crear juegos de simulación de animales. El juego fue lanzado en junio de 2020 y ha ganado más de 1 millón de descargas en Google Play Store. El juego está clasificado 4.4 de 5 estrellas por los usuarios, que elogian sus gráficos, física y jugabilidad.
-animal rebelión batalla simulador mod apk desbloqueado todo
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-
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-
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-
\ No newline at end of file diff --git a/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/modules/vqvae/quantize.py b/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/modules/vqvae/quantize.py deleted file mode 100644 index 9c8caffad7fd4e90b2b5c627dda60d4c9fc496de..0000000000000000000000000000000000000000 --- a/spaces/BernardoOlisan/vqganclip/taming-transformers/taming/modules/vqvae/quantize.py +++ /dev/null @@ -1,329 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -from torch import einsum -from einops import rearrange - - -class VectorQuantizer(nn.Module): - """ - see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py - ____________________________________________ - Discretization bottleneck part of the VQ-VAE. - Inputs: - - n_e : number of embeddings - - e_dim : dimension of embedding - - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 - _____________________________________________ - """ - - # NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for - # a fix and use legacy=False to apply that fix. VectorQuantizer2 can be - # used wherever VectorQuantizer has been used before and is additionally - # more efficient. - def __init__(self, n_e, e_dim, beta): - super(VectorQuantizer, self).__init__() - self.n_e = n_e - self.e_dim = e_dim - self.beta = beta - - self.embedding = nn.Embedding(self.n_e, self.e_dim) - self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) - - def forward(self, z): - """ - Inputs the output of the encoder network z and maps it to a discrete - one-hot vector that is the index of the closest embedding vector e_j - z (continuous) -> z_q (discrete) - z.shape = (batch, channel, height, width) - quantization pipeline: - 1. get encoder input (B,C,H,W) - 2. flatten input to (B*H*W,C) - """ - # reshape z -> (batch, height, width, channel) and flatten - z = z.permute(0, 2, 3, 1).contiguous() - z_flattened = z.view(-1, self.e_dim) - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - - d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ - torch.sum(self.embedding.weight**2, dim=1) - 2 * \ - torch.matmul(z_flattened, self.embedding.weight.t()) - - ## could possible replace this here - # #\start... - # find closest encodings - min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) - - min_encodings = torch.zeros( - min_encoding_indices.shape[0], self.n_e).to(z) - min_encodings.scatter_(1, min_encoding_indices, 1) - - # dtype min encodings: torch.float32 - # min_encodings shape: torch.Size([2048, 512]) - # min_encoding_indices.shape: torch.Size([2048, 1]) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) - #.........\end - - # with: - # .........\start - #min_encoding_indices = torch.argmin(d, dim=1) - #z_q = self.embedding(min_encoding_indices) - # ......\end......... (TODO) - - # compute loss for embedding - loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ - torch.mean((z_q - z.detach()) ** 2) - - # preserve gradients - z_q = z + (z_q - z).detach() - - # perplexity - e_mean = torch.mean(min_encodings, dim=0) - perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) - - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q, loss, (perplexity, min_encodings, min_encoding_indices) - - def get_codebook_entry(self, indices, shape): - # shape specifying (batch, height, width, channel) - # TODO: check for more easy handling with nn.Embedding - min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) - min_encodings.scatter_(1, indices[:,None], 1) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings.float(), self.embedding.weight) - - if shape is not None: - z_q = z_q.view(shape) - - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q - - -class GumbelQuantize(nn.Module): - """ - credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!) - Gumbel Softmax trick quantizer - Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016 - https://arxiv.org/abs/1611.01144 - """ - def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True, - kl_weight=5e-4, temp_init=1.0, use_vqinterface=True, - remap=None, unknown_index="random"): - super().__init__() - - self.embedding_dim = embedding_dim - self.n_embed = n_embed - - self.straight_through = straight_through - self.temperature = temp_init - self.kl_weight = kl_weight - - self.proj = nn.Conv2d(num_hiddens, n_embed, 1) - self.embed = nn.Embedding(n_embed, embedding_dim) - - self.use_vqinterface = use_vqinterface - - self.remap = remap - if self.remap is not None: - self.register_buffer("used", torch.tensor(np.load(self.remap))) - self.re_embed = self.used.shape[0] - self.unknown_index = unknown_index # "random" or "extra" or integer - if self.unknown_index == "extra": - self.unknown_index = self.re_embed - self.re_embed = self.re_embed+1 - print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " - f"Using {self.unknown_index} for unknown indices.") - else: - self.re_embed = n_embed - - def remap_to_used(self, inds): - ishape = inds.shape - assert len(ishape)>1 - inds = inds.reshape(ishape[0],-1) - used = self.used.to(inds) - match = (inds[:,:,None]==used[None,None,...]).long() - new = match.argmax(-1) - unknown = match.sum(2)<1 - if self.unknown_index == "random": - new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) - else: - new[unknown] = self.unknown_index - return new.reshape(ishape) - - def unmap_to_all(self, inds): - ishape = inds.shape - assert len(ishape)>1 - inds = inds.reshape(ishape[0],-1) - used = self.used.to(inds) - if self.re_embed > self.used.shape[0]: # extra token - inds[inds>=self.used.shape[0]] = 0 # simply set to zero - back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) - return back.reshape(ishape) - - def forward(self, z, temp=None, return_logits=False): - # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work - hard = self.straight_through if self.training else True - temp = self.temperature if temp is None else temp - - logits = self.proj(z) - if self.remap is not None: - # continue only with used logits - full_zeros = torch.zeros_like(logits) - logits = logits[:,self.used,...] - - soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard) - if self.remap is not None: - # go back to all entries but unused set to zero - full_zeros[:,self.used,...] = soft_one_hot - soft_one_hot = full_zeros - z_q = einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight) - - # + kl divergence to the prior loss - qy = F.softmax(logits, dim=1) - diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean() - - ind = soft_one_hot.argmax(dim=1) - if self.remap is not None: - ind = self.remap_to_used(ind) - if self.use_vqinterface: - if return_logits: - return z_q, diff, (None, None, ind), logits - return z_q, diff, (None, None, ind) - return z_q, diff, ind - - def get_codebook_entry(self, indices, shape): - b, h, w, c = shape - assert b*h*w == indices.shape[0] - indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w) - if self.remap is not None: - indices = self.unmap_to_all(indices) - one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float() - z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight) - return z_q - - -class VectorQuantizer2(nn.Module): - """ - Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly - avoids costly matrix multiplications and allows for post-hoc remapping of indices. - """ - # NOTE: due to a bug the beta term was applied to the wrong term. for - # backwards compatibility we use the buggy version by default, but you can - # specify legacy=False to fix it. - def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", - sane_index_shape=False, legacy=True): - super().__init__() - self.n_e = n_e - self.e_dim = e_dim - self.beta = beta - self.legacy = legacy - - self.embedding = nn.Embedding(self.n_e, self.e_dim) - self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) - - self.remap = remap - if self.remap is not None: - self.register_buffer("used", torch.tensor(np.load(self.remap))) - self.re_embed = self.used.shape[0] - self.unknown_index = unknown_index # "random" or "extra" or integer - if self.unknown_index == "extra": - self.unknown_index = self.re_embed - self.re_embed = self.re_embed+1 - print(f"Remapping {self.n_e} indices to {self.re_embed} indices. " - f"Using {self.unknown_index} for unknown indices.") - else: - self.re_embed = n_e - - self.sane_index_shape = sane_index_shape - - def remap_to_used(self, inds): - ishape = inds.shape - assert len(ishape)>1 - inds = inds.reshape(ishape[0],-1) - used = self.used.to(inds) - match = (inds[:,:,None]==used[None,None,...]).long() - new = match.argmax(-1) - unknown = match.sum(2)<1 - if self.unknown_index == "random": - new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) - else: - new[unknown] = self.unknown_index - return new.reshape(ishape) - - def unmap_to_all(self, inds): - ishape = inds.shape - assert len(ishape)>1 - inds = inds.reshape(ishape[0],-1) - used = self.used.to(inds) - if self.re_embed > self.used.shape[0]: # extra token - inds[inds>=self.used.shape[0]] = 0 # simply set to zero - back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) - return back.reshape(ishape) - - def forward(self, z, temp=None, rescale_logits=False, return_logits=False): - assert temp is None or temp==1.0, "Only for interface compatible with Gumbel" - assert rescale_logits==False, "Only for interface compatible with Gumbel" - assert return_logits==False, "Only for interface compatible with Gumbel" - # reshape z -> (batch, height, width, channel) and flatten - z = rearrange(z, 'b c h w -> b h w c').contiguous() - z_flattened = z.view(-1, self.e_dim) - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - - d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ - torch.sum(self.embedding.weight**2, dim=1) - 2 * \ - torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n')) - - min_encoding_indices = torch.argmin(d, dim=1) - z_q = self.embedding(min_encoding_indices).view(z.shape) - perplexity = None - min_encodings = None - - # compute loss for embedding - if not self.legacy: - loss = self.beta * torch.mean((z_q.detach()-z)**2) + \ - torch.mean((z_q - z.detach()) ** 2) - else: - loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ - torch.mean((z_q - z.detach()) ** 2) - - # preserve gradients - z_q = z + (z_q - z).detach() - - # reshape back to match original input shape - z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous() - - if self.remap is not None: - min_encoding_indices = min_encoding_indices.reshape(z.shape[0],-1) # add batch axis - min_encoding_indices = self.remap_to_used(min_encoding_indices) - min_encoding_indices = min_encoding_indices.reshape(-1,1) # flatten - - if self.sane_index_shape: - min_encoding_indices = min_encoding_indices.reshape( - z_q.shape[0], z_q.shape[2], z_q.shape[3]) - - return z_q, loss, (perplexity, min_encodings, min_encoding_indices) - - def get_codebook_entry(self, indices, shape): - # shape specifying (batch, height, width, channel) - if self.remap is not None: - indices = indices.reshape(shape[0],-1) # add batch axis - indices = self.unmap_to_all(indices) - indices = indices.reshape(-1) # flatten again - - # get quantized latent vectors - z_q = self.embedding(indices) - - if shape is not None: - z_q = z_q.view(shape) - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q diff --git a/spaces/BetterAPI/BetterChat_new/src/lib/utils/sum.ts b/spaces/BetterAPI/BetterChat_new/src/lib/utils/sum.ts deleted file mode 100644 index 289b70584ef9f7795b1f4b1bf0151237dc2c55ff..0000000000000000000000000000000000000000 --- a/spaces/BetterAPI/BetterChat_new/src/lib/utils/sum.ts +++ /dev/null @@ -1,3 +0,0 @@ -export function sum(nums: number[]): number { - return nums.reduce((a, b) => a + b, 0); -} diff --git a/spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/share/+server.ts b/spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/share/+server.ts deleted file mode 100644 index 5f97daa091152c8074797f1d9f48ebc93fdde718..0000000000000000000000000000000000000000 --- a/spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/share/+server.ts +++ /dev/null @@ -1,54 +0,0 @@ -import { base } from "$app/paths"; -import { PUBLIC_ORIGIN } from "$env/static/public"; -import { collections } from "$lib/server/database.js"; -import type { SharedConversation } from "$lib/types/SharedConversation.js"; -import { sha256 } from "$lib/utils/sha256.js"; -import { error } from "@sveltejs/kit"; -import { ObjectId } from "mongodb"; -import { nanoid } from "nanoid"; - -export async function POST({ params, url, locals }) { - const conversation = await collections.conversations.findOne({ - _id: new ObjectId(params.id), - sessionId: locals.sessionId, - }); - - if (!conversation) { - throw error(404, "Conversation not found"); - } - - const hash = await sha256(JSON.stringify(conversation.messages)); - - const existingShare = await collections.sharedConversations.findOne({ hash }); - - if (existingShare) { - return new Response( - JSON.stringify({ - url: getShareUrl(url, existingShare._id), - }), - { headers: { "Content-Type": "application/json" } } - ); - } - - const shared: SharedConversation = { - _id: nanoid(7), - createdAt: new Date(), - messages: conversation.messages, - hash, - updatedAt: new Date(), - title: conversation.title, - }; - - await collections.sharedConversations.insertOne(shared); - - return new Response( - JSON.stringify({ - url: getShareUrl(url, shared._id), - }), - { headers: { "Content-Type": "application/json" } } - ); -} - -function getShareUrl(url: URL, shareId: string): string { - return `${PUBLIC_ORIGIN || url.origin}${base}/r/${shareId}`; -} diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/install/editable_legacy.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/install/editable_legacy.py deleted file mode 100644 index bebe24e6d3ac321523e0442d28b77b6e6df85970..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/install/editable_legacy.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Legacy editable installation process, i.e. `setup.py develop`. -""" -import logging -from typing import Optional, Sequence - -from pip._internal.build_env import BuildEnvironment -from pip._internal.utils.logging import indent_log -from pip._internal.utils.setuptools_build import make_setuptools_develop_args -from pip._internal.utils.subprocess import call_subprocess - -logger = logging.getLogger(__name__) - - -def install_editable( - *, - global_options: Sequence[str], - prefix: Optional[str], - home: Optional[str], - use_user_site: bool, - name: str, - setup_py_path: str, - isolated: bool, - build_env: BuildEnvironment, - unpacked_source_directory: str, -) -> None: - """Install a package in editable mode. Most arguments are pass-through - to setuptools. - """ - logger.info("Running setup.py develop for %s", name) - - args = make_setuptools_develop_args( - setup_py_path, - global_options=global_options, - no_user_config=isolated, - prefix=prefix, - home=home, - use_user_site=use_user_site, - ) - - with indent_log(): - with build_env: - call_subprocess( - args, - command_desc="python setup.py develop", - cwd=unpacked_source_directory, - ) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/vcs/versioncontrol.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/vcs/versioncontrol.py deleted file mode 100644 index 02bbf68e7ad3ce14f191af24260312e817e12df7..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/vcs/versioncontrol.py +++ /dev/null @@ -1,705 +0,0 @@ -"""Handles all VCS (version control) support""" - -import logging -import os -import shutil -import sys -import urllib.parse -from typing import ( - TYPE_CHECKING, - Any, - Dict, - Iterable, - Iterator, - List, - Mapping, - Optional, - Tuple, - Type, - Union, -) - -from pip._internal.cli.spinners import SpinnerInterface -from pip._internal.exceptions import BadCommand, InstallationError -from pip._internal.utils.misc import ( - HiddenText, - ask_path_exists, - backup_dir, - display_path, - hide_url, - hide_value, - is_installable_dir, - rmtree, -) -from pip._internal.utils.subprocess import ( - CommandArgs, - call_subprocess, - format_command_args, - make_command, -) -from pip._internal.utils.urls import get_url_scheme - -if TYPE_CHECKING: - # Literal was introduced in Python 3.8. - # - # TODO: Remove `if TYPE_CHECKING` when dropping support for Python 3.7. - from typing import Literal - - -__all__ = ["vcs"] - - -logger = logging.getLogger(__name__) - -AuthInfo = Tuple[Optional[str], Optional[str]] - - -def is_url(name: str) -> bool: - """ - Return true if the name looks like a URL. - """ - scheme = get_url_scheme(name) - if scheme is None: - return False - return scheme in ["http", "https", "file", "ftp"] + vcs.all_schemes - - -def make_vcs_requirement_url( - repo_url: str, rev: str, project_name: str, subdir: Optional[str] = None -) -> str: - """ - Return the URL for a VCS requirement. - - Args: - repo_url: the remote VCS url, with any needed VCS prefix (e.g. "git+"). - project_name: the (unescaped) project name. - """ - egg_project_name = project_name.replace("-", "_") - req = f"{repo_url}@{rev}#egg={egg_project_name}" - if subdir: - req += f"&subdirectory={subdir}" - - return req - - -def find_path_to_project_root_from_repo_root( - location: str, repo_root: str -) -> Optional[str]: - """ - Find the the Python project's root by searching up the filesystem from - `location`. Return the path to project root relative to `repo_root`. - Return None if the project root is `repo_root`, or cannot be found. - """ - # find project root. - orig_location = location - while not is_installable_dir(location): - last_location = location - location = os.path.dirname(location) - if location == last_location: - # We've traversed up to the root of the filesystem without - # finding a Python project. - logger.warning( - "Could not find a Python project for directory %s (tried all " - "parent directories)", - orig_location, - ) - return None - - if os.path.samefile(repo_root, location): - return None - - return os.path.relpath(location, repo_root) - - -class RemoteNotFoundError(Exception): - pass - - -class RemoteNotValidError(Exception): - def __init__(self, url: str): - super().__init__(url) - self.url = url - - -class RevOptions: - - """ - Encapsulates a VCS-specific revision to install, along with any VCS - install options. - - Instances of this class should be treated as if immutable. - """ - - def __init__( - self, - vc_class: Type["VersionControl"], - rev: Optional[str] = None, - extra_args: Optional[CommandArgs] = None, - ) -> None: - """ - Args: - vc_class: a VersionControl subclass. - rev: the name of the revision to install. - extra_args: a list of extra options. - """ - if extra_args is None: - extra_args = [] - - self.extra_args = extra_args - self.rev = rev - self.vc_class = vc_class - self.branch_name: Optional[str] = None - - def __repr__(self) -> str: - return f"
-
- """
- )
- with gr.Row():
-
- with gr.Column(scale=55):
- with gr.Group():
- model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
- with gr.Box(visible=False) as custom_model_group:
- custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
- gr.HTML("
-
- Anything V3
-- Demo for Anything V3 -
-You can skip the queue by duplicating this space: 
Custom models have to be downloaded first, so give it some time.
")
-
- with gr.Row():
- prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
- generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-
- image_out = gr.Image(height=512)
- # gallery = gr.Gallery(
- # label="Generated images", show_label=False, elem_id="gallery"
- # ).style(grid=[1], height="auto")
- error_output = gr.Markdown()
-
- with gr.Column(scale=45):
- with gr.Tab("Options"):
- with gr.Group():
- neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-
- # n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
-
- with gr.Row():
- guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
- steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
- with gr.Row():
- width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
- height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
- seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
- with gr.Tab("Image to image"):
- with gr.Group():
- image = gr.Image(label="Image", height=256, tool="editor", type="pil")
- strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
- if is_colab:
- model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
- custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
- # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
-
- inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
- outputs = [image_out, error_output]
- prompt.submit(inference, inputs=inputs, outputs=outputs)
- generate.click(inference, inputs=inputs, outputs=outputs)
-
- ex = gr.Examples([
- [models[0].name, "iron man", 7.5, 50],
-
- ], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False)
-
- gr.HTML("""
-
-
-
- """)
-
-print(f"Space built in {time.time() - start_time:.2f} seconds")
-
-if not is_colab:
- demo.queue(concurrency_count=1)
-demo.launch(debug=is_colab, share=is_colab)
\ No newline at end of file
diff --git a/spaces/ChandraMohanNayal/AutoGPT/data_ingestion.py b/spaces/ChandraMohanNayal/AutoGPT/data_ingestion.py
deleted file mode 100644
index b89a33dafd15c2e7bded0445a741a4a1c47ed417..0000000000000000000000000000000000000000
--- a/spaces/ChandraMohanNayal/AutoGPT/data_ingestion.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import argparse
-import logging
-
-from autogpt.commands.file_operations import ingest_file, search_files
-from autogpt.config import Config
-from autogpt.memory import get_memory
-
-cfg = Config()
-
-
-def configure_logging():
- logging.basicConfig(
- filename="log-ingestion.txt",
- filemode="a",
- format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s",
- datefmt="%H:%M:%S",
- level=logging.DEBUG,
- )
- return logging.getLogger("AutoGPT-Ingestion")
-
-
-def ingest_directory(directory, memory, args):
- """
- Ingest all files in a directory by calling the ingest_file function for each file.
-
- :param directory: The directory containing the files to ingest
- :param memory: An object with an add() method to store the chunks in memory
- """
- try:
- files = search_files(directory)
- for file in files:
- ingest_file(file, memory, args.max_length, args.overlap)
- except Exception as e:
- print(f"Error while ingesting directory '{directory}': {str(e)}")
-
-
-def main() -> None:
- logger = configure_logging()
-
- parser = argparse.ArgumentParser(
- description="Ingest a file or a directory with multiple files into memory. "
- "Make sure to set your .env before running this script."
- )
- group = parser.add_mutually_exclusive_group(required=True)
- group.add_argument("--file", type=str, help="The file to ingest.")
- group.add_argument(
- "--dir", type=str, help="The directory containing the files to ingest."
- )
- parser.add_argument(
- "--init",
- action="store_true",
- help="Init the memory and wipe its content (default: False)",
- default=False,
- )
- parser.add_argument(
- "--overlap",
- type=int,
- help="The overlap size between chunks when ingesting files (default: 200)",
- default=200,
- )
- parser.add_argument(
- "--max_length",
- type=int,
- help="The max_length of each chunk when ingesting files (default: 4000)",
- default=4000,
- )
-
- args = parser.parse_args()
-
- # Initialize memory
- memory = get_memory(cfg, init=args.init)
- print("Using memory of type: " + memory.__class__.__name__)
-
- if args.file:
- try:
- ingest_file(args.file, memory, args.max_length, args.overlap)
- print(f"File '{args.file}' ingested successfully.")
- except Exception as e:
- logger.error(f"Error while ingesting file '{args.file}': {str(e)}")
- print(f"Error while ingesting file '{args.file}': {str(e)}")
- elif args.dir:
- try:
- ingest_directory(args.dir, memory, args)
- print(f"Directory '{args.dir}' ingested successfully.")
- except Exception as e:
- logger.error(f"Error while ingesting directory '{args.dir}': {str(e)}")
- print(f"Error while ingesting directory '{args.dir}': {str(e)}")
- else:
- print(
- "Please provide either a file path (--file) or a directory name (--dir)"
- " inside the auto_gpt_workspace directory as input."
- )
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/ChandraMohanNayal/AutoGPT/run_continuous.sh b/spaces/ChandraMohanNayal/AutoGPT/run_continuous.sh
deleted file mode 100644
index 1f4436c88503172c0578b15a8447ed8268502578..0000000000000000000000000000000000000000
--- a/spaces/ChandraMohanNayal/AutoGPT/run_continuous.sh
+++ /dev/null
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-./run.sh --continuous $@
diff --git a/spaces/Cropinky/esrgan/realesrgan/archs/srvgg_arch.py b/spaces/Cropinky/esrgan/realesrgan/archs/srvgg_arch.py
deleted file mode 100644
index 39460965c9c5ee9cd6eb41c50d33574cb8ba6e50..0000000000000000000000000000000000000000
--- a/spaces/Cropinky/esrgan/realesrgan/archs/srvgg_arch.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn as nn
-from torch.nn import functional as F
-
-
-@ARCH_REGISTRY.register()
-class SRVGGNetCompact(nn.Module):
- """A compact VGG-style network structure for super-resolution.
-
- It is a compact network structure, which performs upsampling in the last layer and no convolution is
- conducted on the HR feature space.
-
- Args:
- num_in_ch (int): Channel number of inputs. Default: 3.
- num_out_ch (int): Channel number of outputs. Default: 3.
- num_feat (int): Channel number of intermediate features. Default: 64.
- num_conv (int): Number of convolution layers in the body network. Default: 16.
- upscale (int): Upsampling factor. Default: 4.
- act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
- """
-
- def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
- super(SRVGGNetCompact, self).__init__()
- self.num_in_ch = num_in_ch
- self.num_out_ch = num_out_ch
- self.num_feat = num_feat
- self.num_conv = num_conv
- self.upscale = upscale
- self.act_type = act_type
-
- self.body = nn.ModuleList()
- # the first conv
- self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
- # the first activation
- if act_type == 'relu':
- activation = nn.ReLU(inplace=True)
- elif act_type == 'prelu':
- activation = nn.PReLU(num_parameters=num_feat)
- elif act_type == 'leakyrelu':
- activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
- self.body.append(activation)
-
- # the body structure
- for _ in range(num_conv):
- self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
- # activation
- if act_type == 'relu':
- activation = nn.ReLU(inplace=True)
- elif act_type == 'prelu':
- activation = nn.PReLU(num_parameters=num_feat)
- elif act_type == 'leakyrelu':
- activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
- self.body.append(activation)
-
- # the last conv
- self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
- # upsample
- self.upsampler = nn.PixelShuffle(upscale)
-
- def forward(self, x):
- out = x
- for i in range(0, len(self.body)):
- out = self.body[i](out)
-
- out = self.upsampler(out)
- # add the nearest upsampled image, so that the network learns the residual
- base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
- out += base
- return out
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/reverseContourPen.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/reverseContourPen.py
deleted file mode 100644
index a3756ab17af131329e88c7136a230a32e3e7a8d5..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/pens/reverseContourPen.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from fontTools.misc.arrayTools import pairwise
-from fontTools.pens.filterPen import ContourFilterPen
-
-
-__all__ = ["reversedContour", "ReverseContourPen"]
-
-
-class ReverseContourPen(ContourFilterPen):
- """Filter pen that passes outline data to another pen, but reversing
- the winding direction of all contours. Components are simply passed
- through unchanged.
-
- Closed contours are reversed in such a way that the first point remains
- the first point.
- """
-
- def __init__(self, outPen, outputImpliedClosingLine=False):
- super().__init__(outPen)
- self.outputImpliedClosingLine = outputImpliedClosingLine
-
- def filterContour(self, contour):
- return reversedContour(contour, self.outputImpliedClosingLine)
-
-
-def reversedContour(contour, outputImpliedClosingLine=False):
- """Generator that takes a list of pen's (operator, operands) tuples,
- and yields them with the winding direction reversed.
- """
- if not contour:
- return # nothing to do, stop iteration
-
- # valid contours must have at least a starting and ending command,
- # can't have one without the other
- assert len(contour) > 1, "invalid contour"
-
- # the type of the last command determines if the contour is closed
- contourType = contour.pop()[0]
- assert contourType in ("endPath", "closePath")
- closed = contourType == "closePath"
-
- firstType, firstPts = contour.pop(0)
- assert firstType in ("moveTo", "qCurveTo"), (
- "invalid initial segment type: %r" % firstType
- )
- firstOnCurve = firstPts[-1]
- if firstType == "qCurveTo":
- # special case for TrueType paths contaning only off-curve points
- assert firstOnCurve is None, "off-curve only paths must end with 'None'"
- assert not contour, "only one qCurveTo allowed per off-curve path"
- firstPts = (firstPts[0],) + tuple(reversed(firstPts[1:-1])) + (None,)
-
- if not contour:
- # contour contains only one segment, nothing to reverse
- if firstType == "moveTo":
- closed = False # single-point paths can't be closed
- else:
- closed = True # off-curve paths are closed by definition
- yield firstType, firstPts
- else:
- lastType, lastPts = contour[-1]
- lastOnCurve = lastPts[-1]
- if closed:
- # for closed paths, we keep the starting point
- yield firstType, firstPts
- if firstOnCurve != lastOnCurve:
- # emit an implied line between the last and first points
- yield "lineTo", (lastOnCurve,)
- contour[-1] = (lastType, tuple(lastPts[:-1]) + (firstOnCurve,))
-
- if len(contour) > 1:
- secondType, secondPts = contour[0]
- else:
- # contour has only two points, the second and last are the same
- secondType, secondPts = lastType, lastPts
-
- if not outputImpliedClosingLine:
- # if a lineTo follows the initial moveTo, after reversing it
- # will be implied by the closePath, so we don't emit one;
- # unless the lineTo and moveTo overlap, in which case we keep the
- # duplicate points
- if secondType == "lineTo" and firstPts != secondPts:
- del contour[0]
- if contour:
- contour[-1] = (lastType, tuple(lastPts[:-1]) + secondPts)
- else:
- # for open paths, the last point will become the first
- yield firstType, (lastOnCurve,)
- contour[-1] = (lastType, tuple(lastPts[:-1]) + (firstOnCurve,))
-
- # we iterate over all segment pairs in reverse order, and yield
- # each one with the off-curve points reversed (if any), and
- # with the on-curve point of the following segment
- for (curType, curPts), (_, nextPts) in pairwise(contour, reverse=True):
- yield curType, tuple(reversed(curPts[:-1])) + (nextPts[-1],)
-
- yield "closePath" if closed else "endPath", ()
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Image-0fe369ad.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Image-0fe369ad.js
deleted file mode 100644
index f201414aaacde6c8751195516c0c6f150bc5afb1..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/Image-0fe369ad.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as h,e as g,s as d,J as n,K as e,p as m,M as i,n as l,A as u}from"./index-1d65707a.js";function f(c){let t,r,s,o;return{c(){t=n("svg"),r=n("rect"),s=n("circle"),o=n("polyline"),e(r,"x","3"),e(r,"y","3"),e(r,"width","18"),e(r,"height","18"),e(r,"rx","2"),e(r,"ry","2"),e(s,"cx","8.5"),e(s,"cy","8.5"),e(s,"r","1.5"),e(o,"points","21 15 16 10 5 21"),e(t,"xmlns","http://www.w3.org/2000/svg"),e(t,"width","100%"),e(t,"height","100%"),e(t,"viewBox","0 0 24 24"),e(t,"fill","none"),e(t,"stroke","currentColor"),e(t,"stroke-width","1.5"),e(t,"stroke-linecap","round"),e(t,"stroke-linejoin","round"),e(t,"class","feather feather-image")},m(a,p){m(a,t,p),i(t,r),i(t,s),i(t,o)},p:l,i:l,o:l,d(a){a&&u(t)}}}class x extends h{constructor(t){super(),g(this,t,null,f,d,{})}}export{x as I};
-//# sourceMappingURL=Image-0fe369ad.js.map
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-085f5795.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-085f5795.js
deleted file mode 100644
index 4f9d7f247f754f5d61a620eb0f78a44760d04085..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-085f5795.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as le,e as re,s as ne,J as G,K as d,p as v,M as Y,n as H,A as C,ak as j,N,T as Q,B as pe,h as L,G as et,O as U,Z as Ze,ar as Ct,z as p,u as $,v as A,y as x,V as St,C as Mt,a7 as we,ai as Et,ao as tt,L as Rt,U as R,Q as F,Y as Dt,a1 as yt,F as te,k as T,o as z,x as B,ap as Ne,aw as Lt,m as me,j as Z,t as K,a9 as jt,ab as Ut,ac as qt,ad as Ft,E as Ht,ae as Nt,q as Wt,r as Ot}from"./index-3370be2a.js";import{f as nt,B as Yt}from"./Button-89624748.js";import{B as Tt}from"./BlockLabel-56db415e.js";import{I as We}from"./Image-93033d87.js";import{C as Xt,i as Jt,U as Pt,W as Qt}from"./StaticImage.svelte_svelte_type_style_lang-e84b963e.js";import{I as Ae}from"./IconButton-abe5ede9.js";import{C as Vt,M as Oe}from"./ModifyUpload-d8fc50ab.js";import{U as Gt}from"./Upload-f29b2460.js";import{u as Zt,S as Kt}from"./ShareButton-39feba51.js";import{E as $t}from"./Empty-585389a4.js";import{D as xt}from"./Download-fdaaf5d4.js";import"./Blocks-f0129fcd.js";import{U as 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Model by Linaqruf
-