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Generate images faster in Stable Diffusion with NVIDIA TensorRT

Hana Trieu — Tue, 07 Nov 2023 06:00:27 +0000

Generate images faster in Stable Diffusion with NVIDIA TensorRT

Stable Diffusion, mainly used to generate images from text prompts, has grown rapidly with numerous new interfaces emerging quicker than people can keep up with. In addition to third-party packages meant to distribute Stable Diffusion widely, GPU manufacturers are also contributing greatly to deliver huge gains in Stable Diffusion speed. This article will introduce the NVIDIA TensorRT extension and explain how it can double the speed of Automatic 1111 Stable Diffusion Web UI, one of the most common implementations of Stable Diffusion. Let’s explore!

GPUs are required for efficient generative AI

Stable Diffusion is a deep learning model utilizing diffusion processes to generate images from text or image prompts. It delivers benefits for creative workflows but is computationally intensive. Generating a small batch of four images can take minutes on nonspecialized hardware like the CPUs, which disrupts workflows and limits developers’ creativity.

CPUs alone struggle with Stable Diffusion because they are not inherently designed for the highly parallel computing required by neural networks. Instead, they are optimized for general-purpose tasks. Stable Diffusion demonstrates the need for specialized hardware like GPUs to power AI applications efficiently. Without GPUs, AI features are generally slow.

NVIDIA TensorRT accelerates Stable Diffusion performance

GeForce RTX GPUs excel at parallel computing necessary for generative AI models. They also contain specialized Tensor Cores that accelerate matrix operations for AI uses. The optimal method to leverage these enhancements is via the NVIDIA TensorRT SDK, which is a high-performance deep learning inference optimizer.

NVIDIA TensorRT provides layer fusion, precision optimization, kernel auto-tuning, and other features that extensively boost efficiency and deep learning speed. This makes TensorRT indispensable for real-time use and demanding tasks like Stable Diffusion.

The figure below compares the performance of Stable Diffusion when using NVIDIA TensorRT vs PyTorch xFormers and CoreML.

Source: NVIDIA

The test used:

Stable Diffusion Web UI from Automatic1111 (for NVIDIA) and Mochi (for Apple)
Image generation: Stable Diffusion 1.5, 512 x 512, batch size 1
Hardware: GeForce RTX 4090 with Intel i9 12900K & Apple M2 Ultra with 76 cores

When applied to Stable Diffusion Web UI image generation, TensorRT substantially accelerated performance. It doubled the number of images generated per minute compared to previously employed PyTorch optimizations.

Implementing NVIDIA TensorRT in a Stable Diffusion Pipeline

NVIDIA has shared a Stable Diffusion TensorRT demo. It provides developers with a guide on preparing diffusion models and accelerating them using the NVIDIA TensorRT extension. This serves as the base for expediting inference in pipelines and software.

By building upon the foundation, the optimizations in the TensorRT pipeline were then applied to a commonly used project by Stable Diffusion developers. Adding TensorRT to the Stable Diffusion Web UI made generative AI more accessible to more people. This expanded the reach and impact of the generative model.

Source: NVIDIA

The introduction of a TensorRT Python package for Windows made the installation process much simpler. This lowered the technical barrier so those with less expertise could readily install and use NVIDIA TensorRT.

Once TensorRT is installed, it provides an intuitive interface that handles the ahead-of-time compilation for TensorRT models. A caching system notably cuts compilation time. These simplifications allow users to concentrate on the core tasks. The integration is flexible too, with dynamic shapes allowing the rendering of various resolutions with little impact on performance. Overall, this extension provides developers with a useful tool to boost their own Stable Diffusion workflows.

Getting started with NVIDIA TensorRT for your Stable Diffusion

To download the Stable Diffusion Web UI TensorRT extension, visit NVIDIA/Stable-Diffusion-WebUI-TensorRT on GitHub. Check out NVIDIA/TensorRT for a demo showing the acceleration of a Stable Diffusion pipeline.

Let’s look more closely at how to install and use the NVIDIA TensorRT extension for Stable Diffusion Web UI using Automatic1111.

Step 1: Install Stable Diffusion web UI from Automatic1111

If you have installed the Stable Diffusion Web UI from Automatic1111, skip to the next step.

These instructions will use the standalone installation.

- Download sd.webui.zip
- Move sd.webui.zip to a location on your local device with enough hard drive space (20GB or more).
- Extract the sd.webui.zip file.
- In the extracted folder, click on the update.bat file which will update the web UI to the latest version (Note: after clicking on the update.bat file, you may see a warning pop-up from Windows. If so, choose More info >> Run Anyway.)
- Close the command prompt window once the files have been updated.
- Click on Run.bat. The installation process will start (the necessary files will be downloaded and installed on your computer.) This process can take a few minutes, and it will also download Stable Diffusion 1.5 (~4GB).
  - - Repeat the above process if the Windows warning appears.
    - The installation finishes when you see this message in the console “Running on local URL: http://127.0.0.1:7860.” A webpage that looks like this will also appear. If it doesn’t appear, you can manually open a browser and go to this URL: http://127.0.0.1:7860.

Step 2: Install the TensorRT extension for Automatic1111

Next, we install the NVIDIA TensorRT extension into our Automatic1111 installation.

Source: CIVITAI

- On the main UI tabs, go to Extensions >> Install from URL:
- In the URL for the extension’s git repository text box, import this URL https://github.com/NVIDIA/Stable-Diffusion-WebUI-TensorRT and click Install. The installation will download and install the necessary files. It will take a few minutes depending on your internet connection.
- When the installation is complete, go to the Installed tab, make sure that the TensorRT extension box is checked >> choose Apply and restart UI button. The UI will refresh for about 2 minutes.

Source: NVIDIA

- After the UI restarts, there will be a new tab for TensorRT tab as follows:

Source: NVIDIA

Step 3: Build TensorRT Engines

TensorRT is the quickest way to run AI on NVIDIA RTX GPUs. It can generate specific optimizations for your exact GPU for the AI model you want to run. These optimizations are called TensorRT Engines. Let’s apply some more steps so that you can generate a generic one or create other custom ones.

Source: CIVITAI

- Go to TensorRT tab.
- The default engine will be selected automatically in the Preset dropdown menu. Select Export Default Engine. This process will take from 4 and 10 minutes, depending on your GPU.
  - - This will create a TensorRT Engine for Stable Diffusion 1.5, for image sizes between 512×512 and 768×768, and for any batch size from 1 to 4.
    - Note that: the first time you run this process, the extension will generate a TensorRT optimized ONNX model. This has to be done just once.
    - The Output section in the bottom part of the screen will show status information on the build process.
    - You can check the console output to see the exact status.
- When the process completes, you can generate other engines that you want. The extension will automatically use the best one from the available ones. Therefore, you can generate as many as you want. Just note that each engine is ~2GB.

- - - Note: There are Static and Dynamic presets. Static presets provide better performance, but only work on the exact settings they are optimized for. Dynamic ones also consume more VRAM.
    - You can see the engines which have been previously at the bottom of the page, under the Available TensorRT engine-profiles section. Click on the checkpoint you want to check the available engines.

Note: this section just updates upon a web UI start. In case you just generated engines, you will need to restart the web UI.

Also, one important note is that TensorRT engines are unique per GPU architecture. When switching between GPU architectures, engines will need to be rebuilt for the different architectures.

Step 4: Activate TensorRT Image Generation

Source: CIVITAI

- On the Main UI tabs, go to Settings >> User Interface.
- Add sd_unet to the list of Quick Settings. This will allow us to select the new TensorRT pipeline to create images:
  - - Select Show all pages >> locate the [info] Quicksettings list
    - Click in the Quicksettings list area >> type: sd_unet
    - Click on the sd_unet item displayed to add it to the Quicksettings.
    - At the top of the Settings page, choose Apply Settings and Reload UI.
    - The top area of the UI will now have a new dropdown for SD Unet. Click on the refresh button to load your new engines.
- In the new SD Unet dropdown, choose the [TRT] v1-5-pruned-emaonly engine listed in the dropdown.

Source: CIVITAI

- - - The engine name shows the Stable Diffusion checkpoint this engine is generated for. You should always verify that the TRT engine matches your currently loaded checkpoint.
    - When choosing the TRT SD_Unet from the dropdown menu, the extension will automatically pick the best TensorRT engine you have built as long as at least one engine exists that matches your output configuration settings.

All are finished! Now, you can go to the text2img tab and start rendering images with optimized performance!

iRender - The best cloud rendering services for Stable Diffusion

Stable Diffusion is primarily designed for single GPU usage; however, with some additional software and configuration, it can take advantage of multiple GPUs. By splitting the work across multiple GPUs, the overall iteration speed can be increased. While most Stable Diffusion implementations are designed to run on a single GPU by default, one commonly used implementation which is Automatic1111 has options to enable multi-GPU support with minimal additional configuration.

Regardless of which implementation of Stable Diffusion you utilize, you can still benefit from iRender’s high-performance GPU machines to accelerate image generation.

We offer flexible configurations of 1, 2, 4, 6, and 8 GPU machines using the top-tier RTX 4090 and RTX 3090. Built with powerful AMD Ryzen Threadripper PRO CPUs with up to 64 cores, 256GB RAM, and 2TB NVMe SSD storage, our machines are capable of generating the most challenging AI arts in Stable Diffusion quickly.

Stable Diffusion available in Pre-installed software | iRender Render Farm

Let’s enjoy our 100% Bonus Program today and generate your Stable Diffusion images faster!

iRender – Happy Rendering, Happy Training

Reference source: NVIDIA, CIVITAI

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Generate images faster in Stable Diffusion with NVIDIA TensorRT

The post Generate images faster in Stable Diffusion with NVIDIA TensorRT appeared first on iRender Cloud Rendering Service.

Will AI occupy 3D arts?

Yen Lily — Wed, 13 Sep 2023 06:00:54 +0000

Will AI occupy 3D arts?

Development of text to image software or programs like MidJourney, Stable Diffusion, DALL-E is making a huge impact on the field of digital arts. Many people complain about it plagiarizing the art sources on the internet, many are afraid of a future there’s no human creativity but only AI reproducing. The fear of AI occupy 3D arts is there, we can feel it. But will it be able to do that? Let’s find out in this article.

The context of AI emergence in 3D arts

AI is now emerging in the design and 3D arts in a way it’s never before. It has great influence and impact that some people even think about being left out because of AI. You can come to any forum or group of digital, 3D arts and encounter many complain about AI images or works. Before, they said that AI images is not good enough with many wrong lighting, shadowing, weird hands or fingers, etc. Now, when AI has fixed those wrong features, digital or 3D artists start to concern about their future. Many companies or even their peers start to use AI to generate ideas faster. Even if those AI works are not good enough, they are still okay for the project, and get approved. That’s when people start to feel nervous.

After facing many opposition and refusal of digital artists about their images being used to ‘feed’ AI, it seems to slow down a little bit, but continue to refine itself. As a result, some people kind of accept it as a new tool for designing, or conceptualizing. Of course, using AI to generate an image and sell it is somewhat not acceptable in a large scale, but using it to generate layout and color schemes is becoming normal.

iRender’s first attempt to use Stable Diffusion

Will AI occupy 3D arts?

The answer is no. Let’s explain it.

What AI can do is to learn and generate images from “old” images available on the internet. So to say in it a way, AI recycles the old ideas and make it a new thing. It can only learn and know the old sources.

What human can do is similar to AI can do, moreover, human can create something new, predicts something based on their senses. That’s what AI cannot learn.

If you think AI is a kid who can learn and replace human in a far future, it’s wrong. AI actually is purely statistic, number, mathematical formula and cases (failure and success). What you see on the internet is successful results from many failure. What makes AI, let’s say text-to-image programs, have this huge progress, is billion and billion of images or training data. It takes times to train massive amount of data over and over, so that AI can learn the ‘old’ things.

iRender’s second attempt to use Stable Diffusion

The hurdles that AI will face in polishing itself are the amount data and senses.

Regarding the amount of data, one can argue that internet has many data, resource and AI can easily learn from them. It’s true, but not in every cases. You can see that the amount of still images are abundant and text-to-image programs can learn from them, making graphic designers question about their future. However, what about niche and more technical field like VFX and simulation? Are there enough or abundant data about pyro simulations of Houdini? Or Phoenix FD, FumeFX, X-particles, Bifrost, Realflow, etc.? I believe there’s not much data about them to train an AI program to create effects like that.

Regarding the senses, it’s harder to explain. Human and AI are different because human have senses. We can feel what is appropriate, what is not, what is right, what is wrong. Take some examples to better explain it.

When we see an images which is related to taboo content (violence, porno, etc.), we know it immediately. Is AI able to detect it? Yes it can, but depending on how it’s trained to be able to detect that. You cannot just put in massive amount of data and expect AI to naturally ‘know’ what is taboo content. You need human to see many content like that, and categorize each one of them, so that you have enough resource about taboo content and train AI about that. Who is willing to see offensive content? Not many. And if you can hire them, it’s still a very traumatizing job which will have a high rate of resignation.

One more thing about designing, it’s client-driven field. It needs human touch to interpret and understand what a unique client requires. You need to work closely with a client and be able to respond to their preferences.

However, there’s one more important aspect about any works which is closely related to client that I think people seem to forget: we need to instruct and recommend a suitable plan to client, and sometimes it’s not based on their ideas. When you are a digital or 3D artist, you are an expert in a way and you can come up with appropriate ideas, based on your experience or study. AI follows what people put in but human will know and understand if that idea is possible to conduct in real life or not. This case could happen a lot in 3D ArchViz.

How should we use AI?

To be honest, I’m not a 3D or digital artist, but I have always appreciated arts. I have followed and witnessed many AI-generated arts which are boring and only human or object focus, no landscape, no style, etc. However I have to admit that it could be a threat to anyone whose job is doing a same thing over and over. They are easy to replace. But innovators will always have a place, and should not be worry.

As what I have discussed in the first part of the article, AI is considered as a tool for conceptualizing by many people now. And I think that it will continue to grow and develop following this path.

Knowing that trend and approach, iRender has released an AI tools on our iRender GPU app. You can try it for free with limitations. Right now we only support this AI tools using Stable Diffusion.

AI tools on iRender GPU app

If you want to use Stable Diffusion, or any other text-to-image software, or train AI, iRender is the best choice. We provide to you high performance and configurable server system for 3D rendering, AI Training, VR & AR, simulation, etc. We believe that design and creative activities are private and personal that only you artists will know what you want with your animation. That’s the reason why we provide a remote server so that you can install any software you need, and do whatever you like with your project.

You will only need to install your software or data for the first time, and next time you connect to the computer everything is ready for use.

Because there’s no statement about Stable Diffusion or any text-to-image software running faster on multiple GPU systems, iRender suggests you select server 3S, which is single RTX4090.

The server specifications are CPU AMD Ryzen Threadripper PRO 3955WX, clock speed at 3.9GHz. RAM is 256GB, hard disk space is 2TB, they can meet almost any requirements of high-resolution art.

Regarding GPU, iRender offers you the RTX4090, the new kind of consumer graphics card right now. VRAM 24GB, raw power could be 2x stronger than RTX3090, it’s the most powerful card right now.

Plus, at iRender, we provide you more support than just those config.

Free and convenient transferring tool

iRender offers a powerful and free file transfer tool: iRender drive for macOS or Linux user. With fast file transfer speed, large data capacity, and completely free. You can transfer all the necessary data into our iRender drive tool at any time without connecting to the server. The data will be automatically synchronized in the Z drive inside the server, ready for you to use.

For Windows users, we recommend using our all-in-one application iRender GPU to work, and you don’t need to come to our website but still can transfer your data, recharge your account and access the machine through the app.

Flexible price

The price of server 3S is $8.2 per hour. However, the server is available for fixed rental when you boot (price at $23.124 for 3 hours rental, about 6% off compared to hourly basis). And you can even find a more attractive option for your big project when you rent it on a daily/weekly/monthly basis, with a discount from 10-20%.

Real human 24/7 support service

Don’t hesitate to contact our human support via livechat on the website when you encounter any issues. We are available 24/7, helping you to handle the problem quickly, effectively, saving you both time and money.

Unique cloud rendering service

When you use iRender cloud rendering service, you are free to install any software or data or tools you need for your project. Our platform is unique, which serves as your second computer from a far. You can rent one remote server, or more than that and still can run them simultaneously to speed up your render time.

With the above advantages and the machine configuration package that iRender has just released, we believe that users will have the most comfortable, quickest, and most effective rendering time.

Register an account today to experience our service and 100% bonus for first deposit of new users. Or contact us via WhatsApp: (+84) 916806116 for advice and support.

Thank you & Happy Rendering!

Source: quora.com, Houdini Artists Facebook group

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The best GPUs for Stable Diffusion

Hana Trieu — Sat, 05 Aug 2023 06:00:18 +0000

The best GPUs for Stable Diffusion

Stable Diffusion, which is mostly used to generate images from texts, is an evolving application of AI technology in the content creation industry. To run Stable Diffusion on your local computer, you need a powerful GPU that can handle its heavy requirements. A powerful GPU allows you to generate images faster, and a more powerful GPU with plenty of VRAM will let you create larger-resolution images even faster. So, what are the best consumer GPUs for Stable Diffusion? Let’s check out the Stable Diffusion performance on some GPUs of NVIDIA and AMD to find the answer.

All about Stable Diffusion

What’s Stable Diffusion?

Stable Diffusion is a machine-learning model. It is increasingly being used in content creation thanks to its ability to generate images from text prompts. What makes Stable Diffusion unique is that it lacks commercially-developed software and instead relies on various open-source applications. Besides, unlike other similar text-to-image models, it is often used locally on local systems rather than using online web services.

Stable Diffusion can run on mid-range GPUs with at least 8GB of VRAM. However, it benefits greatly from powerful modern GPUs with more VRAM.

Stable Diffusion Implementations

You can directly use the Stable Diffusion version developed by Stability AI and Runway. However, most people use a web-based version created by third parties. The most common Stable Diffusion being used are:

- - Automatic 1111: This is mostly used with NVIDIA GPUs, though there are forks that exist for AMD and Apple Silicon. It allows you to use xformers, which can significantly boost performance on NVIDIA GPUs.
  - SHARK: SHARK is an alternative to Automatic 1111. It natively supports NVIDIA and AMD GPUs. Yet, its performance tends to be higher with AMD GPUs while lower with NVIDIA GPUs.
  - Custom: Some people create their own applications with the features they need because Stable Diffusion is publicly available for anyone to use directly.

Each implementation has unique advantages and drawbacks regarding features and usability. From a performance and benchmarking view, Automatic 1111 and SHARK are recommended. Based on the GPU you intend to test, it’s suggested to utilize both Automatic 1111 and SHARK together. Use Automatic 1111 for testing NVIDIA GPUs and SHARK for AMD GPUs.

Note: Stable Diffusion is constantly updated, so the different versions you use can result in changes in performance.

What impact Stable Diffusion’s performance?

Firstly, Stable Diffusion settings & models

The most frequently adjusted settings such as the prompt, negative prompt, cfg scale, and seed – do not meaningfully affect the performance. It takes the same amount of time to generate an image of a dog or mountain landscape. Even the model selected tends to result in only minor differences in generation time. Look at the below images, despite having different prompts and cfg scales, they almost take the exact same amount of time to generate.

Image Credit: Puget Systems

Other settings like the steps, resolution, and sampling method will impact Stable Diffusion’s performance.

- - Steps: Adjusting steps impact the time needed to generate an image but will not alter the processing speed in terms of iterations per second. Though many users choose between 20 and 50 steps, increasing the step count to around 200 tends to produce more consistent results from run to run.
  - Resolution: The image resolution not only has the greatest impact on performance but also influences how much VRAM is needed to generate the image. For benchmarking purposes, you can use a 512×512 resolution to ensure compatibility with various GPU models.
  - Sampling method (Euler, DPM, etc.). It can significantly impact generation time, with some options taking about twice as long as others. “Euler” and “Euler a” are the most widely used and tend to provide the best performance. Other methods like DPM2 tend to take about twice as long. For the purpose of GPU benchmarking, sticking with a variation of Euler for consistency is recommended.

Secondly, the hardware

- - GPU: GPU has the biggest impact on speed and image quality. More powerful GPUs with higher memory bandwidth and more VRAM can generate Stable Diffusion images much faster, especially at higher resolutions. The amount of VRAM on the GPU determines the maximum resolution images that can be generated. At least 8GB is recommended, higher resolutions require 12GB or more.
  - CPU: While the GPU handles most of the heavy lifting, a fast CPU can still improve performance to a lesser extent. CPUs with higher clock speeds and more cores can provide a small boost.
  - RAM: The system memory helps feed data to the GPU, so having at least 16GB of RAM can ensure optimal performance. More RAM, up to 32GB or 64GB, can further improve speeds.

The best GPUs for Stable Diffusion

To know what are the best consumer GPUs for Stable Diffusion, we will examine the Stable Diffusion Performance of these GPUs on its two most popular implementations (their latest public releases).

Many Stable Diffusion implementations show how fast they work by counting the “iterations per second” or “it/s“. Therefore, to check Stable Diffusion Performance, this metric is a commonly used and great measurement. Iterations per second are calculated by dividing the number of iterations by the number of seconds it takes to generate an image. For example, if generating an image with 200 iterations takes 15 seconds, the iterations per second are about 13.3 (that is 200 iterations divided by 15 seconds).

First, let’s look at the benchmark result that Puget Systems tested on the 4000 series GPUs along with the top-tier GPUs from their last three generations of NVIDIA and AMD’s RX 7900 XTX & RX 6900 XT.

Automatic 1111 performance

Image Credit: Puget Systems

Automatic 1111, Stable Diffusion’s most commonly used implementation, usually provides the best performance on NVIDIA GPUs.

NVIDIA clearly outperforms AMD here. Amongst NVIDIA’s GPU list, RTX 4090 is the winner providing the most performance result on Automatic 1111. Even the RTX 3060 Ti is twice as fast as the Radeon GPU. Only the GTX 1080 Ti is worse than the RX 7900 XTX.

The newer 4000 series GPUs offer a clear advantage in image generation speed while also providing a linear increase in performance with price. This is shown by the RTX 4070 Ti being about 5% faster than the previous RTX 3090 Ti, and the RTX 4060 Ti being nearly 43% faster than the 3060 Ti. If you still have a 2000 or 1000 series GPU, even a mid-range 4000 series GPU will provide a noticeable performance boost.

SHARK performance

Image Credit: Puget Systems

Even though SHARK is less commonly used than Automatic 1111, it is preferred by many AMD users. Look at the above benchmark results, it’s clear why.

The RX 7900 XTX sees its performance quadruple with SHARK, resulting in iterations per second similar to the RTX 4090 running 1111. Similarly, the RX 6900 XT has an even larger 1100% performance increase, though this only makes it competitive with the low-end NVIDIA GPUs tested.

With SHARK, NVIDIA GPUs perform around 30% worse than Automatic 1111, despite maintaining the same relative performance.

Important note: It’s very important to use the proper implementation of Stable Diffusion because it can greatly impact performance. It can be from a 30% decrease to a massive 1100% increase! The above GTX 1080 Ti result proves this. It can’t run the SHARK in this testing of Puget Systems.

Wrap-Up

What stands out the most is the huge difference in performance between the various Stable Diffusion implementations. NVIDIA GPUs offer the highest performance on Automatic 1111, while AMD GPUs work best with SHARK. The top GPUs on their respective implementations have similar performance.

If you have not decided to use a particular implementation yet, both NVIDIA and AMD provide great performance at their high-end GPUs. The GeForce RTX 4090 and Radeon RX 7900 XTX both provide around 21 it/s in their preferred implementation of Stable Diffusion.

It is very important to note that Stable Diffusion is a constantly evolving model with a set of tools. How it works today is remarkably different from how it did months ago or will do in the future. Its performance is going to change in the coming months and years. Therefore, the performance results in this article are likely to change over time. As a wise reader, we hope you understand that these benchmark results are for reference only.

If you are interested in testing the performance of your currently-used Stable Diffusion implementation on top-tier GPUs like the RTX 4090, check out our service below.

iRender - The best cloud rendering services for Stable Diffusion

Regardless of which implementation of Stable Diffusion you utilize, you can still benefit from iRender’s high-performance GPU machines to accelerate image generation.

We offer flexible configurations of 1, 2, 4, 6, and 8 GPU machines using the top-tier RTX 4090 and RTX 3090. Built with powerful AMD Ryzen Threadripper PRO CPUs with up to 64 cores, 256GB RAM, and 2TB NVMe SSD storage, our servers can handle even the most demanding AI art in Stable Diffusion quickly.

Stable Diffusion available in Pre-installed software | iRender Render Farm

We have just released an iRender GPU desktop application, allowing you to fully utilize our services easier, and more efficiently. See how our service works:

Quickstart iRender desktop app |All-in-One iRender GPU Application | iRender Render Farm

Let’s enjoy our 100% Bonus Program today and create your Stable Diffusion images faster!

iRender – Happy Rendering, Happy Training

Reference source: pugetsystems.com

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Best hardware for Stable Diffusion Offline

Hana Trieu — Fri, 14 Jul 2023 06:00:36 +0000

Best hardware for Stable Diffusion Offline

AI-generated art using tools like Stable Diffusion has taken the world by storm. Technology has made art generation accessible to everyone with or without an internet connection, allowing them to create whatever they desire. While there are valid concerns about the misuse of AI art, we want to focus on creating AI and using Stable Diffusion locally without an internet connection. Running Stable Diffusion offline requires computer hardware. In this guide, we will recommend the key specs like CPU, GPU, and RAM needed to run it. So without further ado, let’s find the best hardware for Stable Diffusion Offline with iRender.

Source: Stable Diffusion Website

What’s Stable Diffusion?

Stable Diffusion, released in August 2022, is a text-to-image AI model that can generate highly detailed and complex images from simple text prompts. It can be used for inpainting, outpainting, and numerous other tasks. In more detail, Stable Diffusion works by training on millions of images to learn how to generate new images from text descriptions. When you provide a text prompt, the model uses its knowledge of image patterns to produce an image matching that description.

Source: CG Director

Stable Diffusion runs primarily on GPU, so fairly modest hardware can use it. Importantly, it can run locally instead of in the cloud, unlike other text-to-image tools such as DALL-E and Midjourney. This means you store and generate images directly on your computer, providing more privacy and avoiding any potential cloud computing costs. All you need is a capable PC or laptop – there are no paywalls, hidden costs, or usage limits.

What makes Stable Diffusion such a big deal?

Stable Diffusion is significant for several reasons.

It produces images of astonishing complexity and detail that some consider on the edge of copyright boundaries. Its speed and precision threaten to replace many creative jobs that previously took human hours to produce.

It can create logos, banners, montages of all kinds, and more. Its power and capabilities are immense, yet can run on mid-range computers for as little as $600 to $700. With optimization, it may even run on lower-end hardware, though performance will suffer (it won’t be as fast) and certain issues may arise.

Compared to similar applications, Stable Diffusion’s relatively modest hardware requirements have lowered the barrier to entry tremendously. This allows many users to harness its capabilities locally, usurping the status quo of cloud-based image generation tools.

Best hardware for Stable Diffusion Offline

You do not need a high-end PC to run Stable Diffusion locally. A mid-range GPU will suffice, though the speed for generating AI art will not compare to a powerful system. Still, running Stable Diffusion on modest hardware is possible.

The easiest approach is to use pre-made applications with Stable Diffusion integrated. That way you don’t have to write code or manage Python libraries yourself.

Alternatively, you can run Stable Diffusion in the cloud using a web app. Though likely slower than a local install, this option removes setup hassles. Web apps vary in the customization options they provide.

Source: Stable Diffusion Web App

Processor (CPU)

Source: Intel

You do not need a high-end processor to use Stable Diffusion. This is a major benefit. A mid-range four-core, eight-thread CPU will suffice for satisfactory performance.

You can potentially use an even less powerful processor, but the performance may vary and degrade.

Graphics card (GPU)

Source: ROG ASUS

The speed at which your art can be generated depends mainly on your GPU, especially the amount of video memory (VRAM) it has. Only NVIDIA GPU, at the time of this writing, is natively supported. You can try using an AMD or Intel GPU but you’ll have to do some research and find a third-party solution/fork of Stable Diffusion. Though it is not ideal, running Stable Diffusion on non-NVIDIA GPUs still works and is not as difficult as it may sound.

NVIDIA RTX graphics cards offer a plug-and-play experience and will get you up and running quickly. All RTX 2000, 3000, and 4000 series GPUs are supported, meaning even the RTX 3050 will suffice. However, the RTX 3050 is not the best mid-range option due to its 6GB of VRAM.

Stable Diffusion needs as much VRAM as possible, especially for generating 512×512 images and above. Older RTX 2000 series GPUs, regardless of their VRAM, will actually take longer to create AI-generated art compared to RTX 3000 and 4000 series GPUs. Thus, if possible, try to use a newer GPU.

The NVIDIA RTX 3060 with 12GB of VRAM is currently the best option from a price-performance standpoint. It’s relatively affordable, well-rounded, comes with all of NVIDIA’s software and hardware features, and has a surprising amount of video memory for Stable Diffusion and other tasks like content creation or gaming.

Source: Nvidia

If you want to generate images larger than 512×512 and don’t like waiting too long, investing in a powerful GPU with lots of VRAM will yield the best experience.

RAM

Source: GSkill

You can technically run Stable Diffusion with 8GB of RAM. However, 16GB will significantly improve performance and stability. Going with 16GB or higher will ensure Stable Diffusion has enough memory to run without any potential memory-related issues.

Storage

For Stable Diffusion installation, you will need around 10 GB of free storage space, though this amount can vary depending on the specific version you install. The maximum storage requirement could be up to around 25 GB in some cases, for example, if you install a third-party fork that requires more space.

The storage required to store the AI art generated by Stable Diffusion depends heavily on the resolution, size, and number of images you generate. If you plan to generate a lot of AI art, having at least 1-2TB of free storage is advisable. The more images, the higher the resolution and quality, the more storage you will use up over time.

Conclusion

The hardware requirements for creating AI-generated art using Stable Diffusion are actually quite reasonable and manageable. This process is not nearly as demanding as some might expect. Additionally, the availability of various third-party forks of Stable Diffusion allows the software to run on an even wider range of hardware, including systems that may not meet the official requirements.

However, powerful GPUs and high-end PCs can certainly take advantage of Stable Diffusion to produce higher-quality AI art faster.

iRender - The best cloud rendering services for Stable Diffusion

iRender provides high-performance GPU rendering services to unleash creativity for 3D artists. We offer flexible configurations of 1, 2, 4, 6, and 8 GPU servers using the top-tier RTX 4090 and RTX 3090 for accelerated Stable Diffusion AI image generation. Powered by powerful AMD Ryzen Threadripper PRO CPUs with up to 64 cores, 256GB RAM, and 2TB NVMe SSD storage, our servers can handle even the most demanding AI art in Stable Diffusion quickly.

Stable Diffusion available in Pre-installed software | iRender Render Farm

Best of all, we grant you complete access to your rented servers. You can install any software, plugins, or Stable Diffusion versions you need. We treat your rented servers like your personal workstations – no restrictions apply. This freedom enables you, 3D artists, to realize creative visions without limitations.

Make iRender your partner in creativity and unleash your potential with our high-performance GPU servers.

Creating your AI art now!

iRender – Happy Rendering, Happy Training!

Reference sources: CG Director, Stable Diffusion Online

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The post Best hardware for Stable Diffusion Offline appeared first on iRender Cloud Rendering Service.

Best cloud rendering service for Stable Diffusion

Yen Lily — Thu, 08 Jun 2023 06:00:40 +0000

Best cloud rendering service for Stable Diffusion

Stable Diffusion is an AI model which allows you to create images based on keywords you put in. Unlike other AI image generators, such as MidJourney which enables you to create images on the cloud only, Stable Diffusion has both web-based and PC versions.

If you think that AI generating images doesn’t require hardware, you are wrong. To run Stable Diffusion, you will need certain requirements. Let’s explore some recommendations and find the best cloud rendering service for Stable Diffusion.

What is Stable Diffusion?

Before finding the best cloud rendering service for Stable Diffusion, let’s explore “What is Stable Diffusion”. Stable Diffusion is an open-source machine-learning model that can generate, modify art from text. It’s first publicly released in August 2022, and became one of the most popular AI art-generation tools along with MidJourney and DALL-E 2. Like any other AI art generator, it’s been trained on billions of images and if you know how to write a well-worded prompt, Stable Diffusion will return you a great image.

Source: stability.ai

What is Stable Diffusion

Hardware recommendation for Stable Diffusion

Operating system: Windows 8, 10 and 11, MacOS, or Linux.

Graphic Card: minimum 8 GB dedicated memory, recommended RTX2000, 3000 or 4000 series.

CPU (processor): Any modern AMD or Intel CPU

RAM: at least 16 GB

Hard Drive space: 10 GB of free disk space

Installer: Miniconda3, Git, Stable Diffusion files on GitHub, the latest Checkpoints.

How to use Stable Diffusion?

To use Stable Diffusion, there are two ways: one is from DreamStudio, the official web app, or one is from your own computer.

Use Stable Diffusion from DreamStudio

Source: dreamstudio.ai

This is web-based and you will need to login your account in this site: https://dreamstudio.ai/generate

After you login, you can use the tool very easily. They are all on the web, and you can just follow the instructions on the left.

- - - Choose your style: Enhance, Anime, Photographic, etc.
    - Type your prompt: this is the most important part, where you describe how you want your image to look like. Be specific as much as possible.
    - Type negative prompt: you can write anything you don’t want to appear in your image.
    - Upload image: you can upload an image as an example so that the generated art will be based on it.
    - Image count: you choose the number of images you want to generate. Minimum is 1, maximum is 10.

Click the Dream button to generate your art. On this button you will see a number, it’s the credits that your art costs. Wait some seconds, and they will return you generated images.

You can download it if you are satisfied, or modify it if you are not happy with it. You should pay more attention to the prompt, negative prompt and image. Play around with it enough to refine your work and find the best art.

Use Stable Diffusion from your own PC

To install Stable Diffusion on your PC, you can check the instructions in this link.

Using Stable Diffusion on your own PC is completely free. You will need to install Stable Diffusion following the link above. After that, do as follow:

- - - You prepare the text input as a plain text file. Try to describe it as detail as possible like you type the prompt above.
    - Open a command prompt or terminal window and navigate to the Stable Diffusion folder. run the following command:

python inference.py –text_file /path/to/text_input.txt –output_dir /path/to/output_folder

- - - In this command prompt, you should replace /path/to/text_input.txt with the path to your text input file and /path/to/output_folder with the path to the folder where you want to save the generated image.
    - After running Stable Diffusion, you will see all the images located in the output folder.
    - If you want to change the image generated, change the text_file. If you want to change the quality or resolution, change the command line arguments in the “inference.py” file

What iRender can offer to Stable Diffusion users?

iRender provide to you high performance and configurable server system for 3D rendering, AI Training, VR & AR, simulation, etc. We believe that design and creative activities are private and personal that only you artists will know what you want with your animation. That’s the reason why we provide a remote server so that you can install any software you need, and do whatever you like with your project.

You will only need to install your software or data for the first time, and next time you connect to the computer everything is ready for use.

Because there’s no statement about Stable Diffusion running faster on multiple GPU systems, iRender suggests you select server 3S, which is single RTX4090.

Regarding GPU, iRender offers you the RTX4090, the new kind of consumer graphics card right now. VRAM 24GB, raw power could be 2x stronger than RTX3090, it’s the most powerful card right now.

Plus, at iRender, we provide you more support than just those config.

Free and convenient transferring tool

iRender offers a powerful and free file transfer tool: iRender drive. With fast file transfer speed, large data capacity, and completely free. You can transfer all the necessary data into our iRender drive tool at any time without connecting to the server. The data will be automatically synchronized in the Z drive inside the server, ready for you to use.

Flexible price

Real human 24/7 support service

Unique cloud rendering service

With the above advantages and the machine configuration package that iRender has just released, we believe that Stable Diffusion users will have the most comfortable, quickest, and most effective rendering time.

Register an account today to experience our service. Or contact us via WhatsApp: (+84) 916806116 for advice and support.

Thank you & Happy Rendering!

Source: zapier.com, howtogeek.com, pcguide.com

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Keras – Guidelines on installation

Yen Lily — Tue, 16 Aug 2022 06:00:15 +0000

Keras - Guidelines on installation

In the previous article about Keras, we have discovered the introduction and some benefits of Keras. Now, let’s talk more about Keras installation. We will try to help you install it on Windows, Linux and macOS.

Python

Source: python.org

You will need to install Python version 3.5 or higher on your machine before setting up Keras, because Keras is a neural network library based on Python. Go to python.org to download the latest version for your OS, and install it.

If you have already installed Python, then open your terminal and type python, you could see the response similar as specified below:

Python 3.6.5 (v3.6.5:f59c0932b4, Mar 28 2018, 17:00:18)

[MSC v.1900 64 bit (AMD64)] on win32

Type "help", "copyright", "credits" or "license" for more information.

>>>

Basic required installations

Now we move to the installation of some basic requirements like virtual environment and python libraries. You should follow the steps below:

Step 1: Create virtual environment

We will need to create a virtual environment – Virtualenv – to manage python packages for different projects while developing applications. This will help to avoid breaking the packages in the other environments.

There will be 2 commands for Linux/macOS and for Windows.

If you are a Linux or mac OS user, go to your project root directory and type the below command to create virtual environment,

python3 -m venv kerasenv

After executing the above command, “kerasenv” directory is created with bin,lib and include folders in your installation location.

If you are a Windows user, use the below command:

py -m venv keras

Step 2: Activate the environment

In this step, we will configure python and pip executables in your shell path. We also have 2 cases for different OS.

If you are a Linux/ macOS user, move to the folder and type below command (as we have created a virtual environment name “kerasvenv” in the step 1):

$ cd kerasvenv kerasvenv $ source bin/activate

If you are a Windows user, move inside the “kerasenv” folder and type the below command,

.\env\Scripts\activate

Step 3: Python libraries

If you have installed these below libraries, you can skip this step. If you haven’t, please install one by one using commands we provide:

numpy

pip install numpy

pandas

pip install pandas

matplotlib

pip install matplotlib

scipy

pip install scipy

scikit-learn

pip install -U scikit-learn

Scikit-learn is an open source machine learning library and have some requirements:

- - - Python version 3.5 or higher
    - NumPy version 1.11.0 or higher
    - SciPy version 0.17.0 or higher
    - joblib 0.11 or higher.

So make sure you have all of them

Seaborn

pip install seaborn

Seaborn is a library used to visualize your data.

Keras Installation Using Python

Source: keras.io

We have completed all the basic requirements. Now, let’s install Keras using the below command:

pip install keras

Quit virtual environment

You can run this command to quit the environment if you have finished all your changes in your project.

deactivate

Keras installation using Conda

You can also install Keras using Conda. First, just come to www.anaconda.com/distribution and download the version of anaconda based on your OS.

Create a new conda environment

You should launch anaconda prompt, then type the below command in your conda terminal:

conda create --name PythonCPU

This is the CPU instructions. You can also create and install modules using GPU if you want.

Activate conda environment

To activate the environment, use the below command:

activate PythonCPU

Install spyder

Spyder is an IDE for executing python applications. You can install this IDE in the conda environment using the below command:

conda install spyder

Install python libraries

Above we have introduced and instructed you to python libraries needed for keras like numpy, pandas, matplotlib, etc. You can install them using conda, just follow the syntax:

conda install -c anaconda

For example, you want to install pandas

conda install -c anaconda pandas

Install Keras

Now, you can start keras installation using the below command:

conda install -c anaconda keras

Launch spyder

Finally, you can launch spyder in your conda terminal using the below command

spyder

To ensure everything was installed correctly, import all the modules, it will add everything and if anything went wrong, you will get module not found error message.

iRender - Best cloud computing for Keras

We hope that we have delivered some ways to install Keras to you, like using python, using conda. You can follow it on your own computer, or on our remote machine.

At iRender, we provide multiple GPUs for renting with state-of-the-art RTX3090. Our remote machines are Optimized for Scientific Computing, Machine Learning, Deep Learning.

We support many programming languages, libraries, frameworks or API like Keras. You are free to install any of them on our remote servers, add your license (if required) and start your project.

This is our packages tailored for AI/ Deep Learning:

You can also read our article about how RTX3090 be the best choice for AI/ Deep Learning (Is RTX3090 the best GPU for Deep Learning).

Moreover than that, we provide other features like NVLink if you need more VRAM, iRender Drive to transfer and sync files faster, Fixed Rental feature to save credits from 10-20% compared to hourly rental (10% for daily rental, 20% for weekly and monthly rental).

You can register an account today to experience our service and enjoy 20% bonus. Or contact us via WhatsApp: (+84) 916806116 for advice and support.

Thank you & Happy Training!

Source: tutorialspoint.com

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Overview of Keras for beginners

Yen Lily — Tue, 19 Jul 2022 06:00:00 +0000

Overview of Keras for beginners

Nowadays, AI and machine learning continue to grow and become popular. Over time, developers realize they need another branch which is deeper and more specific to solve more difficult and highly complex problems. Deep learning has emerged like that. It’s based on how human brain works, uses neural networks as the core of methodologies. However, to implement neural networks and make operators break down the problem into smaller parts and solve each of them, we will need various libraries like Theano, TensorFlow, Caffe, Mxnet, Keras etc.

Therefore today, let’s take a look at Keras, one of the best deep learning frameworks. We will try to introduce it and explain some benefits when using it.

1. What is Keras

Source: keras.io

Keras is a high-level API designed for Python to implement neural networks easier. It’s developed by Google.

Keras can run on top of the libraries and frameworks such as TensorFlow, Theano, PlaidML, MXNet, CNTK. They are all very powerful libraries but difficult to understand for creating neural networks. Keras, on the other hand, is very beginner-friendly because of its minimal structure that provides a clean and easy way to create deep learning models based on TensorFlow or Theano.

Keras has been adopted by TensorFlow as its official high-level API. When embedded in TensorFlow, it provides inbuilt modules for all neural network computations and therefore can perform deep learning very fast. TensorFlow is very flexible and the primary benefit is distributed computing. You can be flexible and can control over your application, implement your idea in a short time, using Keras, while computation involving tensors, computation graphs, sessions, etc can be custom made using the Tensorflow Core API.

2. Why use Keras?

Keras is very powerful and dynamic. But what make it really stand out and become a popular choice? It comes from Keras’ features set and benefits.

- - - Keras is very simple to learn and understand. Moreover, it’s a consistent and extensible API.
    - Minimal structure makes it easy to achieve the result.
    - It supports many platforms, backends, neural networks models.
    - Keras neural networks are written in Python.
    - It runs on both CPU and GPU smoothly.
    - Large community support with many users and documentation, ready to help than other deep learning frameworks.
    - Keras is the choice of many big names like Netflix, Uber, Square, Yelp, etc. for their products in the public domain.

A simple yet flexible tool for innovative research, a tool which has gained recognition of many developers, we can now understand why it’s used widely. Developers often use it to create deep models for smartphones, for distributed training of deep learning models. Also, they can use it to create and deploy working model in a short time.

3. iRender - Best cloud computing for Keras

In this article, we hope that we deliver to you the definition of Keras and why you should use it. It comes from the versatility and simplicity of Keras. It runs well with Python and TensorFlow, which is the selling point if you want to find a tool for AI, machine learning and especially deep learning.

At iRender, we provide multiple GPUs for renting with state-of-the-art RTX3090. Our remote machines are Optimized for Scientific Computing, Machine Learning, Deep Learning.

This is our packages tailored for AI/ Deep Learning:

You can also read our article about how RTX3090 be the best choice for AI/ Deep Learning (Is RTX3090 the best GPU for Deep Learning).

You can register an account today to experience our service and enjoy 20% bonus. Or contact us via WhatsApp: (+84) 916806116 for advice and support.

Thank you & Happy Training!

Source: simplilearn.com, tutorialspoint.com

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Top programming languages in 2022

Yen Lily — Tue, 05 Jul 2022 06:00:44 +0000

Top programming languages in 2022

Now we have more than 700 programming languages. Of course, there are popular ones and less popular ones, which depend on its history and its capability. With the explosion of technology today, we will expect more and more programming languages to appear.

To prepare for yourself, whether you want to improve your coding skills or start your career in technology, knowing and learning a programming language is an advantage. Let’s start with iRender to explore some very popular and most-used – top programming languages in 2022.

1. Python

Source: python.org

Python is a high-level, multi-purpose programming language. Developed by Guido van Rossum in the 1990s, through the years it has become the most popular programming language today, with more than 80% developers considering it as the main coding language.

The reason for its popularity varies, but the number one reason is its easy learning curve for beginners. Its syntax is very familiar with English, which allows anyone, even a newbie with no programming background, to start creating simple programs.

Moreover, it supports many libraries, which enables you to work easier with artificial intelligence, data science, and machine learning processes.

Python can be a great choice for technologies like AI, ML, Data analytics, scientific computing, as well as back-end web development, automation and desktop apps.

Pros:

- - - Open-source.
    - Easy learning curve
    - Highly scalable.
    - Extensive graphical user interface (GUI) frameworks.
    - Incorporated the variants of Java and C like CPython, Jython, etc.
    - Compatible with Mac and Windows.
    - Ever-growing community support.

Cons:

- - - Slower due to being an interpreted language.
    - Memory-consuming.
    - Not ideal for mobile development.

2. Java

Source: java.com

Along with Python, Java is also a high-level and multi-purpose programming language. Created by James Gosling in 1995, now it has become a secure and scalable tool which enables coders to create all kinds of applications.

Java is highly popular and Java developers are highly in demand because of some reason. First of all, its versatility helps you to write it on one operating system then run it anywhere smoothly.

It is also an easy coding language, which has syntax familiar with real-world tasks and object-oriented structure. It’s used to create applications for many fields in the economy like banking, electronic trading, e-commerce, etc. Moreover, it is used to develop apps for mobile as well, especially for Android.

Pros:

- - - Highly versatile.
    - Easy to learn, write, compile, and debug.
    - The new Java frameworks make developing Java programs faster.
    - Various APIs for application development.
    - Support multithreading. The most secure language.
    - Cross-platform compatibility.

Cons:

- - - Slow execution times.
    - Memory-consuming.

3. JavaScript

Source: javascript.com

To begin with, Java and JavaScript are two different languages. Java is not a shortened version of JavaScript. We might talk about their differences in other articles, but in this part, we will only introduce JavaScript.

Along with HTML and CSS, JavaScript is one of the key programming languages for front-end web development. It was created in 1995 by Netscape.

JavaScript is an easy language to learn, helping you to handle all the dynamic interaction on the web like animated graphics, clickable buttons, pop-ups, alerts, etc. The demand for JavaScript developers is also high due to its high percentage of websites that use JavaScript on the client-side of the webpage (about 97%).

Pros:

- - - Open-source.
    - Easy learning curve.
    - Client-side JavaScript is incredibly fast as it doesn’t require compilation.
    - Works well with other top programming languages.
    - Great for boosting a website’s user experience and engagement.

Cons:

- - - Security issues.
    - Unstable cross-browser performance.

4. C and C++

Source: kdnuggets.com

In spite of being the oldest programming languages (about nearly 50 years), C/C++ are still very relevant now and have gained immense popularity.

They have a steep learning curve, which is suitable for intermediate to advanced level. However, its versatility makes it the best programming language to learn, and its complexity enables you to shine among other developers.

C and C++ are often used to design applications and platforms for real-time systems, embedded systems, mobile apps and video game development, machine learning, etc.

Pros:

- - - Open-source.
    - Highly versatile.
    - Fast and powerful.
    - Allows more control.
    - Works well cross-device and cross-platform.
    - Skills are easily transferable to other languages.

Cons:

- - - It can be challenging for beginners.
    - Misuses of pointers can lead to errors.
    - Its object-oriented structure is less efficient than other OOP-based programming languages.
    - Lack of garbage collection.

5. SQL

Source: mysql.com

If you are interested in data science and statistical computing, SQL (Structured Query Language) is the best choice. Developed by Donald D. Chamberlin and Raymond F. Boyce in 1974, this language has a special role in creating and managing tables and data stored in relational databases.

SQL is extremely easy to learn as the syntax uses common English words.

The demand for SQL developers is high, because pretty much every field related to large amounts of data dealing will need it.

Pros:

- - - Faster query processing.
    - Excellent portability.
    - Robust security.
    - Highly interactive.
    - Large user community.

Cons:

- - - Not open source.
    - Not the best programming language if you want to build applications.

iRender – Powerful cloud computing service

This article is dedicated to introducing some of the best programming languages. They can be used for many purposes, from web developing, mobile and desktop apps, to data science, AI, machine learning and deep learning. They can serve each of your needs and you should select the one with an easy learning curve first, then you can continue with others which are more complex.

At iRender, we provide multiple GPUs for renting with state-of-the-art RTX3090. Our remote machines are Optimized for Scientific Computing, Machine Learning, Deep Learning.

We support many programming languages and many libraries and IDEs. This is our packages tailored for AI/ Deep Learning:

Plus, at iRender, we provide you more support than just those hardware.

NVLink available for better performance

If 24GB VRam is not enough for your project, we always have NVLink to help you access more than that. You can access this article to know how to set up NVLink on our machine.

Free and convenient transferring tool

iRender offers a powerful and free file transfer tool: Gpuhub Sync. With fast file transfer speed, large data capacity, and completely free. You can transfer all the necessary data into our Gpuhub Sync tool at any time without connecting to the server. The data will be automatically synchronized in the Z drive inside the server, ready for you to use.

Flexible price

Besides hourly rental, you can always save from 10% to 20% with our Fixed Rental feature. For those who’s in need of a server for more than a day, or have extremely large projects, we advise to choose a daily/ weekly/monthly rental package. The discount is attractive (up to 10% for daily packages, 20% on weekly and monthly packages), and you don’t have to worry about over-charging if you forget to shutdown the server.

Real human 24/7 support service

Users can access our web-based online platform and use multiple nodes to render at the same time. Hence, with us, it does not matter where you are present – as long as you are connected to the internet, you can access and enjoy the 24/7 rendering services that we provide, and if you meet any issue, our real human 24/7 support team is always ready to support you.

With these advantages and no hidden cost when using our machine, we believe it’s worth trying your project. You can create an account via this link to experience our service. And don’t hesitate to contact us via WhatsApp: (+84) 916806116 for advice and support.

Thank you & Happy Training!

Source: hostinger.com, geeksforgeeks.org, kdnuggets.com

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The risks of AI in 2022

Yen Lily — Tue, 21 Jun 2022 06:00:15 +0000

The risks of AI in 2022

AI – Artificial Intelligence has been increasingly developing in our modern life, with many applications help change the world. However, some, if not many people, think that it’s very dangerous. The rapid grow of AI, if is used in the wrong way, will cause serious impacts.

There are many theories about Destructive superintelligence – artificial general intelligence that’s created by humans and escapes our control to wreak havoc. It could happen or not happen. Right now we are still in the very early stages of artificial intelligence, so let’s talk about some of the ways it warns us about current and near future of pitfalls – The risks of AI.

Job automation

Source: pcma.org

Job automation could be the most immediate concern now because it has partially replaced human in some certain types of jobs. For example, if you works in a job that performs predictable and repetitive tasks, there’s high risk that it will be done using AI. There are many reports from Brookings Institution show that AI will soon replace 70% of tasks like retails sales, market analysis, hospitality, warehouse labour, and even white-collar jobs.

It’s not only the repetitive jobs that is slowly replaced by AI, but also professions that require graduate degrees and additional post-college training aren’t immune to AI displacement. John C. Havens, author of Heartificial Intelligence: Embracing Humanity and Maximizing Machines, has interviewed one law firm about machine learning. He found out a case where the head of the firm could replace 10 people (with salary $100,000 each) by a software which costs only $200,000. The software saves him money, and moreover it helps increase productivity by 70 percent and eradicate roughly 95 percent of errors. What would be his selection? It’s easy to see.

Technology strategist Chris Messina said that accounting will be the next one who is at risk. One human auditor can read through a lot of information then make decision based on that. Once AI is able to comb through thousand of data and quickly make a deliver, we can see the unnecessity of human auditors.

Since good data available in these cases is abundant, algorithms are becoming just as good at diagnostics as the experts. The difference is: the algorithm can draw conclusions in a fraction of a second, and it can be reproduced inexpensively all over the world. Soon everyone, everywhere could have access to the same quality of top expert in radiology diagnostics, and for a low price.

Privacy, Security and the rise of “Deepfakes”

Source: vox.com

Job loss is one of many potential risks of AI. We will talk about another issue which is slowly emerging right now – privacy and security.

On February 2018, 26 researchers from 14 institutions addressed a paper titled “The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation“. In there, they discussed that if AI is not strictly and ethically controlled, it could threaten digital security, physical security and political security.

A prime example is China’s “Orwellian” use of facial recognition technology in offices, schools and other venues. What will happen if bad actors exploit this AI-analyzed monitoring?

The concern goes even higher when we see the rise of so-called audio and video deepfakes. It’s created by manipulating voices and likenesses, making waves across the world. For example, one can make a video or an audio clip of any politician spouted racist or sexist views when in fact they do not. If the clip is quality enough to fool the general public and avoid detection, it could be a huge issue. We cannot know what is real what is fake, and cannot rely on anything. That is a disaster.

AI bias and widening socioeconomic inequality

AI is developed by humans, and humans are inherently biased, therefore AI could be biased too. According to Princeton computer science professor Olga Russakovsky, AI researchers are primarily male, “who come from certain racial demographics, who grew up in high socioeconomic areas, primarily people without disabilities.” So it’s hard to think broadly about world issues. They can try to understand the social dynamics of the world, but they are humans and can’t avoid the fact that they have bias.

Autonomous weapons and a potential AI arms race

Elon Musk's Last Warning About Artificial Intelligence

Elon Musk once said that AI is more dangerous than nukes. Some would not agree with him, but around 30,000 AI/robotics researchers and others certainly think so. Why? There are two reasons.

The first one is what if AI can decide its own action to achieve its goal. And what if its decision is to launch nukes or any other weapons of mass destruction without human intervention. The result is disastrous.

The second one is AI can be manipulated by terrorists or dictators. The researchers wrote that “Autonomous weapons are ideal for tasks such as assassinations, destabilizing nations, subduing populations and selectively killing a particular ethnic group.” This would cause unnecessary AI arms race.

Mitigating the risk of AI

It is really hard to mitigate the risks of AI, as one can develop it safely, but others can still use it in a wrong way. Different people will make different choices. However, we will need at least a public body who can regulate how AI is used, but doesn’t hold back the progress of basic technology.

Final thought

Through time, we can see AI as revolutionary or world-changing, but it does contain some drawbacks and all the risks of AI in 2022. They could be very damaging and dangerous, and we will need to be really careful when developing and using them.

iRender is currently providing GPU Cloud for AI/DL service so that users can train their models. With our high configuration and performance machines (RTX3090), you can install any software you need for your demands. With just a few clicks, you are able to get access to our machine and take full control of it. Your model training will speed up times faster.

Moreover than that, we provide other features like NVLink if you need more VRAM, Gpuhub Sync to transfer and sync files faster, Fixed Rental feature to save credits from 10-20% compared to hourly rental (10% for daily rental, 20% for weekly and monthly rental).

Thank you & Happy Training!

Source: builtin.com

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How to Install C and C++ support in Visual Studio

Yen Lily — Tue, 07 Jun 2022 06:00:35 +0000

How to Install C and C++ support in Visual Studio

An IDE is a coding environment that includes both an editor and a language-specific toolchain. It also contains many other features to support many aspects of software development. Today, we will talk about Visual Studio IDE and how to install it and Microsoft C/C++ tools.

You can use Visual Studio IDE to edit, debug, build code, publish an app. And Visual Studio is not just a normal IDE, with normal features like editor and debugger, it can provide your more than that such as compilers, code completion tools, graphical designers, etc. to enhance the software development process. Now, let’s get started.

Visual Studio 2022 Installation

This article is only about how you can install Visual Studio 2022 on Windows. If you want to know the instruction for Mac, please see Install Visual Studio for Mac.

Step 1 - Check your system if it is ready for Visual Studio

Before installing Visual Studio on your PC, you will need to reboot your PC to make sure there’s no pending installs or updates that hinder the installation of Visual Studio. Then, you should check some of the information below:

- - - System requirement to know if your PC support Visual Studio 2022.
    - Latest Windows updates to ensure your PC has latest security updates and system requirement for Visual Studio.
    - Free up space by running the Disk Cleanup app to remove unnecessary files from your %SystemDrive%.

Step 2 - Download Visual Studio

In this step, we download the bootstrapper file of Visual Studio. You should go to Visual Studio download page, select the edition you need and click the Free trial or Free download button.

Step 3 - Install the Visual Studio installer.

Now you should find the bootstrapper file of Visual Studio you have downloaded then install it. It will install on your PC a lightweight installer which contains everything you need.

First, you double click the bootstrapper that matches or is similar to one of the following files:

- - - vs_community.exe for Visual Studio Community
    - vs_community.exe for Visual Studio Professional
    - vs_community.exe for Visual Studio Enterprise

Then you will receive a User Account Control notice. Please click Yes to run the bootstrapper.

Choose Continue if it asks you to acknowledge the Microsoft License Terms and the Microsoft Privacy Statement.

Step 4 - Choose workloads

Once you installed the installer of Visual Studio, you can select the workloads and feature sets that you want. Let’s see how you do it.

Below is the Installing Visual Studio screen you will find the workload on it.

Source: docs.microsoft.com

For core C and C++ support, choose Workloads menu > Desktop development with C++ (the third one). It will show you details on the right side of screen. You can select or deselect optional components in this list.

You can see that it has other workloads support other kinds of development, like the Universal Windows Platform development, Mobile development with C++, etc.

After you choose the workload(s) and optional components you want, choose Install.

Before that, there are some optional installation you can set up:

Choose individual components (Optional)

There’s a case when you want to add more components than what is available in workload installs list, you can do so by using the Individual components tab. You can install and add individual components that you need, and follow the prompts.

Install language packs (Optional)

When running for the first time, the installer program will match the language of your OS. If you want to change the language of Visual Studio, choose Language packs > select language you want, then follow the prompts.

Source: docs.microsoft.com

Change the installation location (Optional)

If you install Visual Studio and its stuffs on one drive, it will be heavy. You can reduce it by install Visual Studio on one drive runs it the fastest, and move the download cache, shared components, SDKs, and tools to different drives.

Step 5 - Start developing

After completing installation of Visual Studio, you can now choose Launch to start developing.

To start a new project, choose “Create a new project“. Then you type the app you want to create in the search box and select a template.

Now Visual Studio will open a new project for you, and you can code.

Start your project in C++ and Visual Studio now with iRender

Above is the guideline on how to install C and C++ support for Visual Studio. You can follow and do it on your PC, or on our Windows server.

At iRender, we are proud to be one of the few who provides multiple GPUs for renting with state-of-the-art RTX3090. Our remote machines are Optimized for Scientific Computing, Machine Learning, Deep Learning with Windows and Linux OS.

We support many programming language including C/C++ and IDE like Visual Studio. You can install them on our server, then start to develop your program now.

This is our packages tailored for AI/ Deep Learning:

You can also read our article about how RTX3090 be the best choice for AI/ Deep Learning (Is RTX3090 the best GPU for Deep Learning).

Moreover than that, we provide other features like NVLink if you need more VRAM, Gpuhub Sync to transfer and sync files faster, Fixed Rental feature to save credits from 10-20% compared to hourly rental (10% for daily rental, 20% for weekly and monthly rental).

You can register an account today to experience our service. Or contact us via WhatsApp: (+84) 916806116 for advice and support.

Thank you & Happy Training!

Source: docs.microsoft.com

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The post How to Install C and C++ support in Visual Studio appeared first on iRender Cloud Rendering Service.