SECourses - Generative AI

You've likely encountered those awe-inspiring AI-generated videos. Well, the wait is over. The renowned Kling AI is now accessible globally at no cost. In this instructional video, I'll demonstrate how to sign up for Kling AI for free using just an email address and utilize its remarkable text-to-video animation, image-to-video animation, text-to-image, and image-to-image functionalities. This video will present unfiltered results, giving you an accurate understanding of the model's quality and capabilities, unlike those highly selective example demonstrations. Nevertheless, #KlingAI remains the sole #AI model that rivals OpenAI's #SORA and is available for actual use. 🔗 Kling AI's Official Website ⤵️ ▶️ https://www.klingai.com/ 🔗 SECourses Discord Server for Comprehensive Assistance ⤵️ ▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388 🔗 Our GitHub Repository ⤵️ ▶️ https://github.com/FurkanGozukara/Stable-Diffusion 🔗 Our Reddit Community ⤵️ ▶️ https://www.reddit.com/r/SECourses/ 0:00 Introducing Kling AI - the premier video generator AI model 0:28 Step-by-step guide to registering for Kling AI at no cost 1:17 Generating a prompt idea using Claude 3.5 for free to apply on Kling AI for video creation 1:54 Evaluating a challenging prompt on Kling AI with various settings 2:56 Techniques for optimizing LLM-generated prompts for text-to-video (AI) platforms 3:20 Daily free video generation limit and Kling AI's credit system explanation 3:48 Instructions for creating multiple videos simultaneously 4:21 Maximum video duration possible with Kling AI's free version 4:54 Comparing different configuration tests for text-to-video generation on Kling AI 5:38 Crafting a prompt for image-to-image video / animation generation 5:55 Guide to producing an AI video from an input image 7:11 Comparative analysis of various configuration tests for image-to-video generation on Kling AI 8:50 Optimal image-to-video animation configuration for Kling AI 9:45 Tutorial on using Kling AI's text-to-image feature Kuaishou Initiates Global Public Beta Testing for 'Kling AI', Enhancing Model Capabilities Kuaishou Technology (HKD Counter Stock Code: 01024 / RMB Counter Stock Code: 81024) (including its subsidiaries and consolidated affiliated entities, hereafter referred to as "Kuaishou" or the "Company"), a prominent content community and social platform, recently announced significant upgrades to the foundation model of its "Kling AI" (可灵AI) video generation system. The beta version is now accessible to users worldwide via web portal (Chinese version: https://klingai.kuaishou.com/ ; English version: https://klingai.com/). To meet the growing needs of its extensive content creator base, Kuaishou has not only launched beta testing of Kling AI for a broad audience but also introduced a subscription program for mainland China users, offering more tailored features across different subscription tiers. The Company plans to roll out international subscriptions in the near future. Improved Foundation Model Enhances User Experience In the month since its debut, Kling AI has undergone multiple enhancements. With the subscription program's introduction, the foundation model now offers even more advanced features. The latest upgrades significantly improve overall video quality, with generated videos showing enhanced composition, color tone, and overall aesthetics. Motion performance has also been substantially improved, featuring greater range and accuracy of movement. Previous versions of Kling AI offered capabilities like image-to-video generation and video extension. At the recent World Artificial Intelligence Conference, Kling AI was officially launched on the web with several new features, including extending text-to-video generation duration to 10 seconds. The latest upgrade promises an even better overall AI video-generating experience. Full Beta Testing Launched with Limited-Time Subscription Discount As the world's first accessible, real-image-level video generation large model for average users, Kling AI has been immensely popular since opening applications on June 6. After receiving over one million applications, more than 300,000 users were granted early access. With today's announcement, Kuaishou has fully launched the beta version to everyone, bringing the exciting Kling AI experience to a wider audience. Users will receive 66 daily "Inspiration Credits" that can be used to redeem specific functions or value-added services on the Kling AI platform, equivalent to producing about six free videos. Alongside the upgrade, Kling AI has officially launched an all-new subscription program for mainland China users. Users can choose from three subscription tiers on Kling AI's official website: Gold, Platinum and Diamond, priced at RMB66, RMB266 and RMB666 per month, respectively.

Zero-shot cutting-edge Deepfake / Face Swap software Rope Pearl now incorporates TensorRT and instantaneous webcam processing. In this tutorial, I'll demonstrate how to effortlessly install Rope Pearl Live on your device and utilize the webcam Deepfake feature. The installer will handle the entire setup process automatically, and I'll guide you through using this impressive new version. #rope #deepfake #faceswap 🔗 Rope Pearl Live Installation Scripts ⤵️ ▶️ https://www.patreon.com/posts/most-advanced-1-105123768 🔗 Step-by-Step Requirements Guide ⤵️ ▶️ https://youtu.be/-NjNy7afOQ0 🔗 Primary Windows Tutorial ⤵️ ▶️ https://youtu.be/RdWKOUlenaY 🔗 Cloud Mass Computing Guide (Mac users can follow this tutorial) ⤵️ ▶️ https://youtu.be/HLWLSszHwEc 🔗 Official Rope Pearl Live GitHub Repository ⤵️ ▶️ https://github.com/argenspin/Rope-Live 🔗 SECourses Discord Server for Comprehensive Support ⤵️ ▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388 🔗 Our GitHub Repository ⤵️ ▶️ https://github.com/FurkanGozukara/Stable-Diffusion 🔗 Our Reddit Community ⤵️ ▶️ https://www.reddit.com/r/SECourses/ 0:00 Overview of Rope Pearl real-time live face swapper 1:20 Downloading and installing Rope Pearl Live on Windows 5:21 Confirming installation and saving logs 5:51 Launching and operating Rope Pearl Live post-installation 6:29 Configuring settings and initiating face swap 7:38 Preserving processed videos with swapped faces 8:24 Rope Pearl processing speed using CUDA on RTX 3090 TI 8:41 TensorRT installation and performance boost 10:34 Manual addition of TensorRT libraries to system environment variables Path 11:10 Real-time processing speed with TensorRT 12:13 TensorRT VRAM usage 12:56 Utilizing webcam for instant face swapping and creating modified webcam output Inswapper and Deepfakes: The Progress of Synthetic Media In recent times, the domain of artificial intelligence and computer vision has witnessed remarkable progress, resulting in the creation of increasingly advanced technologies for manipulating and generating media. Two notable examples of these innovations are Inswapper and deepfakes. This article will delve into these concepts thoroughly, examining their origins, technological foundations, applications, and the ethical issues they present. Deepfakes: The Cornerstone Deepfakes, a blend of "deep learning" and "fake," denote synthetic media where an individual's appearance is substituted with another's in existing images or videos. This technology emerged in late 2017 when an anonymous Reddit user known as "deepfakes" began sharing altered pornographic videos featuring celebrity faces seamlessly integrated onto adult film actors' bodies. The technology underlying deepfakes relies on deep learning algorithms, particularly generative adversarial networks (GANs). GANs comprise two neural networks: a generator that produces fake images, and a discriminator that attempts to differentiate between real and fake images. Through an iterative process, the generator enhances its ability to create convincing fakes, while the discriminator improves at detecting them. Inswapper: A Specialized Instrument Inswapper, an abbreviation of "face inswapping," is a more recent and specialized tool within the broader category of deepfake technologies. Developed by ArcFace, Inswapper concentrates specifically on face swapping in images and videos. It employs advanced machine learning techniques to achieve highly realistic face replacements with minimal input data. Key attributes of Inswapper include: Efficiency: Inswapper can produce high-quality face swaps using a single reference image, unlike many deepfake algorithms that require extensive training data. Expression preservation: The technology aims to maintain the original facial expressions and movements of the target video, enhancing the realism of the swap. Real-time capability: Some versions of Inswapper can perform face swaps in real-time, opening up possibilities for live applications. Enhanced identity transfer: Inswapper focuses on transferring the core identity features of a face while maintaining the original head pose, lighting, and expression. Technical Aspects Both deepfakes and Inswapper rely on deep learning techniques, but their specific implementations differ: Deepfakes typically utilize autoencoders or GANs. The process involves training the model on thousands of images of both the source and target faces, learning to reconstruct and swap facial features. Inswapper often employs more advanced architectures like 3D face reconstruction models and identity disentanglement networks. These allow for more precise face swapping with less training data. Recent advancements in both technologies have incorporated attention mechanisms, which help in preserving fine details and improving overall realism.

LivePortrait AI: Transform Static Photos into Talking Videos. It now supports Video-to-Video conversion and Superior Expression Transfer at Remarkable Speed A new tutorial is anticipated to showcase the latest changes and features in V3, which includes Video-to-Video functionality and additional enhancements. The V3 update introduces video-to-video capabilities. If you're seeking a one-click installation method for the open-source, zero-shot image-to-animation application LivePortrait on Windows for local use, this tutorial is ideal. This guide introduces you to the cutting-edge, open-source image-to-animation generator, Live Portrait. Simply provide a static image and a driving video, and within seconds, you'll have an impressively functional animation. LivePortrait is incredibly fast and adept at preserving the facial expressions from the input video. The results will astound you. 🔗 Windows Local Installation Tutorial ️⤵️ ▶️ https://youtu.be/FPtpNrmuwXk 🔗 LivePortrait Installers Scripts ⤵️ ▶️ https://www.patreon.com/posts/107609670 🔗 Requirements Step by Step Tutorial ⤵️ ▶️ https://youtu.be/-NjNy7afOQ0 🔗 Cloud Massed Compute, RunPod & Kaggle Tutorial (Mac users can follow this tutorial) ⤵️ ▶️ https://youtu.be/wG7oPp01COg 🔗 Official LivePortrait GitHub Repository ⤵️ ▶️ https://github.com/KwaiVGI/LivePortrait 🔗 SECourses Discord Channel to Get Full Support ⤵️ ▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388 🔗 Paper of LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control ⤵️ ▶️ https://arxiv.org/pdf/2407.03168 The video tutorial covers the following topics: 0:00 Introduction to the state-of-the-art image-to-animation open-source application LivePortrait 2:20 Downloading and installing the LivePortrait Gradio application on your computer 3:27 Requirements for the LivePortrait application and their installation 4:07 Verifying correct installation of requirements 5:02 Confirming successful installation and saving installation logs 5:37 Launching the LivePortrait application post-installation 5:57 Showcasing additional materials provided, including portrait images, driving video, and rendered videos 7:28 Using the LivePortrait application 8:06 VRAM usage when generating a 73-second animation video 8:33 Animating the first image 8:50 Monitoring the animation process status 10:10 Completion of the first animation video rendering 10:24 Resolution of the rendered animation videos 10:45 Original output resolution of LivePortrait 11:27 Improvements and new features coded on top of the official demo app 11:51 Default save location for generated animated videos 12:35 The effect of the Relative Motion option 13:41 The effect of the Do Crop option 14:17 The effect of the Paste Back option 15:01 The effect of the Target Eyelid Open Ratio option 17:02 How to join the SECourses Discord channel

With the V3 update adding video-to-video functionality, this tutorial is perfect for those interested in using LivePortrait but lacking a powerful GPU, Mac users, or those preferring cloud-based solutions. This guide will walk you through the one-click installation and usage of the LivePortrait application on #MassedCompute, #RunPod, and even a free #Kaggle account. After following this tutorial, you'll find running LivePortrait on cloud services as straightforward as running it on your own computer. LivePortrait is the latest state-of-the-art static image to talking animation generator, surpassing even paid services in both speed and quality. 🔗 Cloud (no-GPU) Installations Tutorial for Massed Compute, RunPod and free Kaggle Account ️⤵️ ▶️ https://youtu.be/wG7oPp01COg 🔗 LivePortrait Installers Scripts ⤵️ ▶️ https://www.patreon.com/posts/107609670 🔗 Windows Tutorial - Watch To Learn How To Use ⤵️ ▶️ https://youtu.be/FPtpNrmuwXk 🔗 Official LivePortrait GitHub Repository ⤵️ ▶️ https://github.com/KwaiVGI/LivePortrait 🔗 SECourses Discord Channel to Get Full Support ⤵️ ▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388 🔗 Paper of LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control ⤵️ ▶️ https://arxiv.org/pdf/2407.03168 🔗 Upload / download big files / models on cloud via Hugging Face tutorial ⤵️ ▶️ https://youtu.be/X5WVZ0NMaTg 🔗 How to use permanent storage system of RunPod (storage network volume) ⤵️ ▶️ https://youtu.be/8Qf4x3-DFf4 🔗 Massive RunPod tutorial (shows runpodctl) ⤵️ ▶️ https://youtu.be/QN1vdGhjcRc The cloud tutorial video covers these topics: 0:00 Introduction to the state-of-the-art image-to-animation open-source application LivePortrait cloud tutorial 2:26 Installing and using LivePortrait on MassedCompute with a special discount coupon code 4:28 Applying the special Massed Compute coupon for a 50% discount 4:50 Setting up the ThinLinc client to connect and use the Massed Compute virtual machine 5:33 Configuring the ThinLinc client's synchronization folder for file transfer 6:20 Transferring installer files to the Massed Compute sync folder 6:39 Connecting to the initialized Massed Compute virtual machine and installing the LivePortrait app 9:22 Starting and using the LivePortrait application on MassedCompute post-installation 10:20 Launching a second instance of LivePortrait on the second GPU on Massed Compute 12:20 Locating generated animation videos and downloading them to your computer 13:23 Installing LivePortrait on the RunPod cloud service 14:54 Selecting the appropriate RunPod template 15:20 Setting up RunPod proxy access ports 16:21 Uploading installer files to RunPod's JupyterLab interface and initiating the installation process 17:07 Starting the LivePortrait app on RunPod after installation 17:17 Launching LivePortrait on the second GPU as a second instance 17:31 Connecting to LivePortrait through RunPod's proxy connection 17:55 Animating the first image on RunPod with a 73-second driving video 18:27 Demonstrating the app's impressive speed in animating a 73-second video 18:41 Understanding and resolving input upload errors with an example 19:17 One-click download of all generated animations on RunPod 20:28 Monitoring the progress of animation generation 21:07 Installing and using LivePortrait for free on a Kaggle account with impressive speed 24:10 Generating the first animation on Kaggle after installation and launch 24:22 Ensuring complete upload of input images and videos to avoid errors 24:35 Tracking the animation status and progress on Kaggle 24:45 Monitoring GPU, CPU, RAM, and VRAM usage during the LivePortrait animation process on Kaggle 25:05 Downloading all generated animations on Kaggle with one click 26:12 Restarting the LivePortrait app on Kaggle without reinstallation 26:36 Joining the SECourses Discord channel for support and discussion

Tutorial on Youtube : https://youtu.be/XFUZof6Skkw In this video, I demonstrate how to install and use #SwarmUI on cloud services. If you lack a powerful GPU or wish to harness more GPU power, this video is essential. You'll learn how to install and utilize SwarmUI, one of the most powerful Generative AI interfaces, on Massed Compute, RunPod, and Kaggle (which offers free dual T4 GPU access for 30 hours weekly). This tutorial will enable you to use SwarmUI on cloud GPU providers as easily and efficiently as on your local PC. Moreover, I will show how to use Stable Diffusion 3 (#SD3) on cloud. SwarmUI uses #ComfyUI backend. 🔗 The Public Post (no login or account required) Shown In The Video With The Links ➡️ https://www.patreon.com/posts/stableswarmui-3-106135985 🔗 Windows Tutorial for Learn How to Use SwarmUI ➡️ https://youtu.be/HKX8_F1Er_w 🔗 How to download models very fast to Massed Compute, RunPod and Kaggle and how to upload models or files to Hugging Face very fast tutorial ➡️ https://youtu.be/X5WVZ0NMaTg 🔗 SECourses Discord ➡️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388 🔗 Stable Diffusion GitHub Repo (Please Star, Fork and Watch) ➡️ https://github.com/FurkanGozukara/Stable-Diffusion Coupon Code for Massed Compute : SECourses Coupon works on Alt Config RTX A6000 and also RTX A6000 GPUs 0:00 Introduction to SwarmUI on cloud services tutorial (Massed Compute, RunPod & Kaggle) 3:18 How to install (pre-installed we just 1-click update) and use SwarmUI on Massed Compute virtual Ubuntu machines like in your local PC 4:52 How to install and setup synchronization folder of ThinLinc client to access and use Massed Compute virtual machine 6:34 How to connect and start using Massed Compute virtual machine after it is initialized and status is running 7:05 How to 1-click update SwarmUI on Massed Compute before start using it 7:46 How to setup multiple GPUs on SwarmUI backend to generate images on each GPU at the same time with amazing queue system 7:57 How to see status of all GPUs with nvitop command 8:43 Which pre installed Stable Diffusion models we have on Massed Compute 9:53 New model downloading speed of Massed Compute 10:44 How do I notice GPU backend setup error of 4 GPU setup 11:42 How to monitor status of all running 4 GPUs 12:22 Image generation speed, step speed on RTX A6000 on Massed Compute for SD3 12:50 How to setup and use CivitAI API key to be able to download gated (behind a login) models from CivitAI 13:55 How to quickly download all of the generated images from Massed Compute 15:22 How to install latest SwarmUI on RunPod with accurate template selection 16:50 Port setup to be able to connect SwarmUI after installation 17:50 How to download and run installer sh file for RunPod to install SwarmUI 19:47 How to restart Pod 1 time to fix backends loading forever error 20:22 How to start SwarmUI again on RunPod 21:14 How to download and use Stable Diffusion 3 (SD3) on RunPod 22:01 How to setup multiple GPU backends system on RunPod 23:22 Generation speed on RTX 4090 (step speed for SD3) 24:04 How to quickly download all generated images on RunPod to your computer / device 24:50 How to install and use SwarmUI and Stable Diffusion 3 on a free Kaggle account 28:39 How to change model root folder path on SwarmUI on Kaggle to use temporary disk space 29:21 Add another backend to utilize second T4 GPU on Kaggle 29:32 How to cancel run and start SwarmUI again (restarting) 31:39 How to use Stable Diffusion 3 model on Kaggle and generate images 33:06 Why we did get out of RAM error and how we fixed it on Kaggle 33:45 How to disable one of the back ends to prevent RAM error when using

Do not skip any part of this tutorial to master how to use Stable Diffusion 3 (SD3) with the most advanced generative AI open source APP SwarmUI. Automatic1111 SD Web UI or Fooocus are not supporting the #SD3 yet. Therefore, I am starting to make tutorials for SwarmUI as well. #StableSwarmUI is officially developed by the StabilityAI and your mind will be blown after you watch this tutorial and learn its amazing features. StableSwarmUI uses #ComfyUI as the back end thus it has all the good features of ComfyUI and it brings you easy to use features of Automatic1111 #StableDiffusion Web UI with them. I really liked SwarmUI and planning to do more tutorials for it. 🔗 The Public Post (no login or account required) Shown In The Video With The Links ➡️ https://www.patreon.com/posts/stableswarmui-3-106135985 0:00 Introduction to the Stable Diffusion 3 (SD3) and SwarmUI and what is in the tutorial 4:12 Architecture and features of SD3 5:05 What each different model files of Stable Diffusion 3 means 6:26 How to download and install SwarmUI on Windows for SD3 and all other Stable Diffusion models 8:42 What kind of folder path you should use when installing SwarmUI 10:28 If you get installation error how to notice and fix it 11:49 Installation has been completed and now how to start using SwarmUI 12:29 Which settings I change before start using SwarmUI and how to change your theme like dark, white, gray 12:56 How to make SwarmUI save generated images as PNG 13:08 How to find description of each settings and configuration 13:28 How to download SD3 model and start using on Windows 13:38 How to use model downloader utility of SwarmUI 14:17 How to set models folder paths and link your existing models folders in SwarmUI 14:35 Explanation of Root folder path in SwarmUI 14:52 VAE of SD3 do we need to download? 15:25 Generate and model section of the SwarmUI to generate images and how to select your base model 16:02 Setting up parameters and what they do to generate images 17:06 Which sampling method is best for SD3 17:22 Information about SD3 text encoders and their comparison 18:14 First time generating an image with SD3 19:36 How to regenerate same image 20:17 How to see image generation speed and step speed and more information 20:29 Stable Diffusion 3 it per second speed on RTX 3090 TI 20:39 How to see VRAM usage on Windows 10 22:08 And testing and comparing different text encoders for SD3 22:36 How to use FP16 version of T5 XXL text encoder instead of default FP8 version 25:27 The image generation speed when using best config for SD3 26:37 Why VAE of the SD3 is many times better than previous Stable Diffusion models, 4 vs 8 vs 16 vs 32 channels VAE 27:40 How to and where to download best AI upscaler models 29:10 How to use refiner and upscaler models to improve and upscale generated images 29:21 How to restart and start SwarmUI 32:01 The folders where the generated images are saved 32:13 Image history feature of SwarmUI 33:10 Upscaled image comparison 34:01 How to download all upscaler models at once 34:34 Presets feature in depth 36:55 How to generate forever / infinite times 37:13 Non-tiled upscale caused issues 38:36 How to compare tiled vs non-tiled upscale and decide best 39:05 275 SwarmUI presets (cloned from Fooocus) I prepared and the scripts I coded to prepare them and how to import those presets 42:10 Model browser feature 43:25 How to generate TensorRT engine for huge speed up 43:47 How to update SwarmUI 44:27 Prompt syntax and advanced features 45:35 How to use Wildcards (random prompts) feature 46:47 How to see full details / metadata of generated images 47:13 Full guide for extremely powerful grid image generation (like X/Y/Z plot) 47:35 How to put all downloaded upscalers from zip file 51:37 How to see what is happening at the server logs 53:04 How to continue grid generation process after interruption 54:32 How to open grid generation after it has been completed and how to use it 56:13 Example of tiled upscaling seaming problem 1:00:30 Full guide for image history 1:02:22 How to directly delete images and star them 1:03:20 How to use SD 1.5 and SDXL models and LoRAs 1:06:24 Which sampler method is best 1:06:43 How to use image to image 1:08:43 How to use edit image / inpainting 1:10:38 How to use amazing segmentation feature to automatically inpaint any part of images 1:15:55 How to use segmentation on existing images for inpainting and get perfect results with different seeds 1:18:19 More detailed information regarding upscaling and tiling and SD3 1:20:08 Seams perfect explanation and example and how to fix it 1:21:09 How to use queue system 1:21:23 How to use multiple GPUs with adding more backends 1:24:38 Loading model in low VRAM mode 1:25:10 How to fix colors over saturation 1:27:00 Best image generation configuration for SD3 1:27:44 How to apply upscale to your older generated images quickly via preset 1:28:39 Other amazing features of SwarmUI 1:28:49 Clip tokenization and rare token OHWX

V-Express tutorial for cloud (Massed Compute, RunPod & free Kaggle). Ever wished your static images could talk like magic? Meet V-Express, the groundbreaking open-source and free tool that breathes life into your photos! Whether you have an audio clip or a video, V-Express animates your images to create stunning talking avatars. Just like the acclaimed D-ID Avatar, Wav2Lip, and Avatarify, V-Express turns your still photos into dynamic, speaking personas, but with a twist—it's completely open-source and free to use! With seamless audio integration and the ability to mimic video expressions, V-Express offers an unparalleled experience without any cost or restrictions. Experience the future of digital avatars today—let's dive into how you can get started with V-Express and watch your images come alive! 1-Click V-Express Installers Scripts ⤵️ https://www.patreon.com/posts/105251204 Windows Tutorial ⤵️ https://youtu.be/xLqDTVWUSec Hugging Face Uploading & Downloading Tutorial ⤵️ https://youtu.be/X5WVZ0NMaTg Massed Compute Register and Login ⤵️ https://vm.massedcompute.com/signup?linkId=lp_034338&sourceId=secourses&tenantId=massed-compute Official Rope GitHub Repository ⤵️ https://github.com/tencent-ailab/V-Express SECourses Discord Channel to Get Full Support ⤵️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388 0:00 Introduction to the V-Express with demo showcase 2:35 How to download installer scripts 2:54 How to install V-Express on Massed Compute with an extremely cheap price for 48GB RTX A6000 cloud machine 4:25 How to install and setup ThinLinc client to control Massed Compute virtual cloud machine 5:05 How to setup synchronization folder on ThinLinc client to transfer files between your PC and remote cloud machine 6:05 How to connect Massed Compute initialized virtual machine (VM) and start installing V-Express on Massed Compute 6:32 Amazing special features of Massed Compute SECourses image / machine 7:05 How to transfer installer files to start installation on Massed Compute 9:05 How to start / re-start V-Express on Massed Compute after installed and use it to animate images into talking avatars 13:11 Where are the generated video files are saved and how to download them to our computer from remote cloud machine 14:11 Generating a 1-minute long animated video and how much VRAM it uses 15:36 How to install V-Express image to talking avatars APP on a RunPod cloud virtual machine 16:10 Which Pod selection settings and configuration you should do 16:44 Which RunPod template you have to use for V-Express APP 17:46 How to upload installer files to the RunPod machine and start installation process 18:23 How to start / re-start V-Express on RunPod after installed and use it to animate images into talking avatars 20:06 How to download all of the generated videos on RunPod to your computer 20:48 How to install and use V-Express on a Free Kaggle account with our Kaggle notebook2 24:18 How to download generated videos on Kaggle to your computer at once 🚨 You Won't BELIEVE What This A.I. Can Do! 🤖 Introducing the MIND-BLOWING Talking Avatar Generator That Will Leave You SPEECHLESS! 😱 Prepare to have your mind absolutely BLOWN by the most incredible A.I. technology you've ever seen! 🤯 We're talking about a revolutionary Talking Avatar Generator that creates stunningly realistic video avatars with fully synced audio - and the results are nothing short of JAW-DROPPING! 😲 But the BEST part? You can use this cutting-edge tool for SUPER CHEAP on INSANELY POWERFUL cloud GPUs like the RTX A6000 🚀 - we're talking just 31 CENTS per hour! 💸 Or if you're feeling EXTRA THRIFTY, you can even use it FOR FREE with our exclusive Kaggle Notebook! 🎉 Don't waste THOUSANDS on expensive CGI when you can create STUNNING, PROFESSIONAL-GRADE talking avatars in MINUTES with our one-click installation on Windows, Massed Compute, RunPod, and Kaggle! 💻 Our STEP-BY-STEP tutorials make it FOOLPROOF, even if you're a total beginner! 🙌 Seriously, the quality of these A.I. avatars will leave you ASTOUNDED 🤩 - it's like something straight out of a Hollywood blockbuster! 🎥 You won't be able to tell the difference between our virtual humans and REAL actors! 😎 But don't just take our word for it - we've got TONS of mind-melting demo videos and test configurations for you to try out yourself! 🎬 Trust us, once you see the UNBELIEVABLE results this Talking Avatar Generator can achieve, you'll be HOOKED! 🎣 So what are you waiting for? 🏃‍♂️💨 Smash that link in the description and prepare to have your world ROCKED by the FUTURE of A.I.! 🔮 And don't forget to obliterate that like button and SLAM subscribe for more MIND-BOGGLING A.I. content! 🔥

YouTube Tutorial : https://youtu.be/xLqDTVWUSec Ever wished your static images could talk like magic? Meet V-Express, the groundbreaking open-source and free tool that breathes life into your photos! Whether you have an audio clip or a video, V-Express animates your images to create stunning talking avatars. Just like the acclaimed D-ID Avatar, Wav2Lip, and Avatarify, V-Express turns your still photos into dynamic, speaking personas, but with a twist—it's completely open-source and free to use! With seamless audio integration and the ability to mimic video expressions, V-Express offers an unparalleled experience without any cost or restrictions. Experience the future of digital avatars today—let's dive into how you can get started with V-Express and watch your images come alive! 1-Click V-Express Installers Scripts ⤵️ https://www.patreon.com/posts/105251204 Requirements Step by Step Tutorial ⤵️ https://youtu.be/-NjNy7afOQ0 Massed Compute Register and Login ⤵️ https://vm.massedcompute.com/signup?linkId=lp_034338&sourceId=secourses&tenantId=massed-compute Official Rope GitHub Repository ⤵️ https://github.com/tencent-ailab/V-Express SECourses Discord Channel to Get Full Support ⤵️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388 0:00 Introduction to the V-Express with demo showcase 1:23 The features of the V-Express talking avatars app 2:02 How to download and install V-Express on Windows 3:29 Which requirements are necessary and how to install and verify them 4:56 How to uninstall my scripts installed apps 5:35 How to save installation logs to send me in case of any error 6:05 How to start using V-Express Gradio app after installation and the settings of the app 8:14 Explanation of auto cropping 9:05 Generating first example video and how much VRAM it is using and how much time it is taking 10:57 The location of where generated videos are saved Transforming Static Images into Dynamic Videos: A Comprehensive Guide In the evolving landscape of digital content creation, transforming static images into dynamic, talking avatars is no longer a complex task reserved for professionals. With advancements in AI technology, applications like Tencent AI Lab's V-Express, D-ID, and other commercial tools have made this process accessible to everyone. This article delves into the functionalities of these applications, focusing on how they can be utilized to create engaging video content from static images, thereby enhancing your content's SEO and overall impact. Introduction to Tencent AI Lab V-Express Tencent AI Lab V-Express is an innovative open-source application designed to convert static images into talking avatars. This tool supports both audio and video inputs, making it versatile for various content creation needs. Here's a step-by-step guide on how to install and use V-Express on Windows. Installation Guide Preparation: Download the V-Express zip files and demo images from the provided links. Avoid using space characters in folder names to prevent path handling issues. Extraction: Extract the downloaded zip files into your chosen directory. Installation: Double-click the windows_install.bat file. This will install the application into a virtual environment, ensuring it doesn’t conflict with other applications. Configuration: Verify the installation of Python 3.10.11, Git, FFmpeg, CUDA 11.8, and C++ tools by running specific commands in CMD. Execution: Once installed, double-click the windows_start.bat file to start the application. Using V-Express Upload: Upload a static image and an audio or video file. Settings: Configure settings like retarget strategy, video width, and height, VRAM usage, and face focus expansion. Generation: Click generate to create the video. The application will save the output in the specified folder. Exploring D-ID and Other Commercial Apps D-ID D-ID is a commercial application known for its advanced capabilities in transforming static images into videos. It offers features like: Realistic Animations: Creates highly realistic talking avatars. Customization: Allows users to customize facial expressions and movements. Ease of Use: User-friendly interface suitable for non-technical users. Other Notable Apps Synthesia: Specializes in creating AI-generated videos with human-like avatars. It’s widely used for corporate training and marketing. Reallusion iClone: Offers robust tools for 3D animation and character creation, making it ideal for professional animators. DeepBrain: Focuses on converting text to speech with animated avatars, perfect for educational content.

#Rope is the newest 1-Click, most easy to use, most advanced open source Deep Fake application. It has been just published yesterday. In this tutorial I will show you how to use Rope Pearl DeepFake application. Rope is way better than Roop, #Roop Unleashed and #FaceFusion. It supports multi-face Face Swapping and making amazing DeepFake videos so easily with 1-Click. Select video, select faces and generate your DeepFake 4K ultra-HD video. 1-Click Rope Installers Scripts ⤵️ https://www.patreon.com/posts/most-advanced-1-105123768 How To Install Requirements Tutorial (Python, Git, FFmpeg, CUDA, C++ Tools) ⤵️ https://youtu.be/-NjNy7afOQ0 Official Rope GitHub Repository ⤵️ https://github.com/Hillobar/Rope Rope's Author Donation Link - Support Him For Better APP ⤵️ https://www.paypal.com/donate/?hosted_button_id=Y5SB9LSXFGRF2 0:00 Example Deepfake video from movie Inglourious Basterds 2009 0:21 Introduction to the most easy to use and most advanced 1-Click Deepfake application Rope Pearl 0:53 How to download 1-Click installer scripts and start installing Rope Pearl 1:34 What are the requirements of Deepfake app Rope Pearl and how to check and install them 1:44 How to check and verify your Python, Git, CUDA and FFmpeg installations 3:42 Example images and a test video that I prepared and sharing 4:10 How to start Rope Deepfake application after the installation has been completed 4:27 How to use Rope Pearl Deepfake application - first select videos and images folders 5:00 How to refresh and re-populate selected videos and faces folders 5:26 How to set the outputs folder where the Deepfake videos and images will be saved 5:45 How Rope Pearl the most advanced Deepfake application work, select input video and target faces 6:34 How to make swapped, deep faked faces HD from low resolution 7:01 How to further improve face quality with face restoration AI models automatically 7:49 How to make additional changes to fix artifacts and mistakes in the Deepfaked video 8:27 Support link to support author of Rope developer 8:37 How to test and see each changes effect immediately 9:00 The tests and configurations I have pre-prepared for you 9:19 How to use Face Parser to fix the mouth movement 9:53 How to reduce VRAM usage and increase processing speed with number of threads 10:13 How to export and save Deepfake applied new video 12:12 Where will be the output / exported video saved 12:33 Important face detection models Retina face, Yolo and SCRDF - try them if face detection fails 13:34 How to understand when the Deepfake video processing is completed 13:59 Properties of the generated Deepfake video, e.g. resolution, bitrate 14:24 How to Deep Fake / Face Swap images not videos 15:30 How to save deep faked images 15:43 What is auto swap and how to use it 16:10 How to find best working face before start processing the video 17:13 How to automatically install and use Rope DeepFake AI on a Linux system Deepfake Tutorial: Rope-Pearl Application for Face Swapping in Videos and Images Installation Download the installer files from the provided link in the video description Extract the files to your desired installation location (e.g., rope_ai folder) Ensure you have the necessary prerequisites installed: Python 3.10.11 Git FFmpeg CUDA Run the install.bat file to start the installation process The installer will download the necessary models and set up a virtual environment Using Rope-Pearl for Video Face Swapping Open Rope-Pearl by double-clicking the windows_start.bat file Select the videos folder containing your input video Select the faces folder containing the face images you want to use for swapping Click "Start Rope" to refresh the interface with the latest files Select the output folder where the processed video will be saved Select the video you want to modify Click "Find Faces" to detect faces in the video Select the face you want to replace and the face you want to replace it with Adjust the Swapper Resolution to enhance the quality (up to 512 pixels) Enable the restorer and choose GPEN512 for best results Fine-tune the blend ratio to make the face swap look more natural Enable strength and adjust size border distance to fix errors Use the Occluder and Face Parser to improve mouth movements and fix other issues Set the number of threads based on your GPU's capabilities Choose the output video quality Click the record icon and then play to start processing the video with the face swap Using Rope-Pearl for Image Face Swapping Switch to the image tab in Rope-Pearl Select your source image and click "Find Faces" Select the face you want to replace and the target face Enable "Swap Faces" and adjust settings as needed (Swapper Resolution, Restorer, etc.) Use the "Auto Swap" feature to automatically apply the selected face to new images Click "Save Image" to save the face-swapped image to the output folder Additional Tips and Information Try different face detection models (Retina Face, Yolo v8, SCRDF)

1-Click fresh Automatic1111 SD Web UI Installer Script with TensorRT and more ⤵️ https://www.patreon.com/posts/86307255 🚀 UNLOCK INSANE SPEED BOOSTS with NVIDIA's Latest Driver Update or not? 🚀 Are you ready to turbocharge your AI performance? Watch me compare the brand-new NVIDIA 555 driver against the older 552 driver on an RTX 3090 TI for #StableDiffusion. Discover how TensorRT and ONNX models can skyrocket your speed! Don't miss out on these game-changing results! 0:00 Introduction to the NVIDIA newest driver update performance boost claims 0:25 What I am going to test and compare in this video 1:11 How to install latest version of Automatic1111 Web UI 1:40 The very best sampler of Automatic1111 for Stable Diffusion image generation / inference 1:57 Automatic1111 SD Web UI default installation versions 2:12 RTX 3090 TI image generation / inference speed for SDXL model with default Automatic1111 SD Web UI installation 2:22 How to see your NVIDIA driver version and many more info with nvitop library 2:40 Default installation speed for NVIDIA 551.23 driver 2:53 How to update Automatic1111 SD Web UI to the latest Torch and xFormers 3:05 Which CPU and RAM used to conduct these speed tests CPU-Z results 3:54 nvitop status while generating an image with Stable Diffusion XL - SDLX on Automatic1111 Web UI 4:10 The new generation speed after updating Torch (2.3.0) and xFormers (0.0.26) to the latest version 4:20 How to install TensorRT extension on Automatic1111 SD Web UI 5:28 How to generate a TensorRT ONNX model for huge speed up during image generation / inference 6:39 How to enable SD Unet selection to be able to use TensorRT generated model 7:13 TensorRT pros and cons 7:38 TensorRT image generation / inference speed results 8:09 How to download and install the latest NVIDIA driver properly and cleanly on Windows 9:03 Repeating all the testing again on the newest NVIDIA driver (555.85) 10:06 Comparison of other optimizations such as SDP attention or doggettx 10.:35 Conclusion of the tutorial NVIDIA's Latest Driver: Does It Really Deliver? In this video, we dive deep into NVIDIA's newest driver update, comparing the performance of driver versions 552 and 555 on an RTX 3090 TI running Windows 10. We'll explore the claims of speed improvements, particularly with #ONNX runtime and TensorRT integration, using the popular Automatic1111 Web UI. What You'll Learn: Driver Comparison: Direct performance comparison between NVIDIA drivers 552 and 555. Setup and Installation: Step-by-step guide on setting up a fresh #Automatic1111 Web UI installation, including the latest versions of Torch and xFormers. ONNX and TensorRT Models: Detailed testing of default and TensorRT-generated models to measure speed differences. Hardware Specifications: Insights into the hardware used for testing, including CPU and memory configurations. Testing Procedure: Initial Setup: Fresh installation using a custom installer script which includes necessary models and styles. Initial speed test with default settings and configurations. Driver 552 Performance: Speed testing on driver 552 with default models and configurations. Detailed performance metrics and image generation speed analysis. Upgrading to Latest Torch and xFormers: Updating to the latest versions of Torch (2.3.0) and xFormers (0.0.26). Performance testing after updates and comparison with initial setup. TensorRT Installation and Testing: Installing TensorRT extension and generating TensorRT models. Overcoming common installation errors and optimizations. Speed testing with TensorRT models and analysis of performance improvements. Upgrading to Driver 555: Step-by-step guide on downloading and installing NVIDIA driver 555. Performance comparison between driver 552 and 555. Analyzing the impact on speed and efficiency. Results and Conclusions: Performance Metrics: Detailed analysis of speed improvements (or lack thereof) with the newest NVIDIA driver. TensorRT Benefits: How TensorRT models significantly boost performance. Driver Update Impact: Understanding the real-world impact of updating to the latest NVIDIA driver.

If you are wondering which Graphic to purchase to run recent Artificial Intelligence (#AI), Machine Learning (#ML), and Deep Learning models on your GPU with CUDA, then this is the right video for you. I have purchased the cheapest and yet largest VRAM having GPU #RTX3060. In this video I am going to compare the performance of Gainward RTX 3060 Ghost 12 GB GPU with MSI GTX 1050 Ti OC 4 GB GPU model and with my CPU which is Core i7 10700F running at 4.59 GHz. For performance tests, I will use OpenAI’s very newest AI model release Whisper. So this is a video of GTX 1050 Ti vs Core i7 10700F vs RTX 3060 in terms of Machine Learning applications performance. Whisper is used for transcribing speech into text in 99 languages. You can check out my tutorial educational video regarding Whisper here: https://youtu.be/msj3wuYf3d8 Also, in this video, I am doing a box opening of Gainward RTX 3060 Ghost. Moreover, I do a physical comparison of RTX 3060 with GTX 1050 Ti. Furthermore, I use an AC power meter plug (digital wattmeter - watt energy meter) to calculate GTX 1050 Ti, RTX 3060, and Core i7 10700F power consumption. I am very satisfied with the performance of RTX 3060. Moreover, it is even able to run the large model of the Whisper which is the best-released model. Please join Our Discord server for asking questions and discussions: https://discord.gg/rfttctFewW Please follow us on Twitter: https://twitter.com/SECourses Please follow us on Facebook: https://www.facebook.com/OfficialSECourses If you are interested in programming our playlists will teach you how to program and code from scratch: https://www.youtube.com/c/SECourses/playlists [1] Introduction to Programming Full Course with C# playlist [2] Advanced Programming with C# Full Course Playlist [3] Object Oriented Programming Full Course with C# playlist [4] Asp.NET Core V5 - MVC Pattern - Bootstrap V5 - Responsive Web Programming with C# Full Course Playlist [5] Artificial Intelligence (AI) and Machine Learning (ML) Full Course with C# Examples playlist [6] Software Engineering Full Course playlist [7] Security of Information Systems Full Course playlist Thumbnail source : https://www.freepik.com/free-vector/isometric-computer-hardware-parts-set-with-monitor-system-unit-electronic-components-details-isolated_9647137.htm

If you want to transcribe your videos and audio into text for free but with high quality, you have come to the correct video. In this tutorial video, I will guide you on how to use #OpenAI #Whisper model. I will show you how to install and run Open AI's Whisper from scratch. I will demonstrate to you how to convert audio/speech into text. Whisper is a general-purpose speech recognition model released for free by Open AI. I claim that Whisper is the best available Speech-to-Text model (Natural Language Processing - #NLP) released to public usage including premium paid ones such as Amazon Web Services, Microsoft Azure Cloud Platform, or Google Cloud API. And Whisper is free to use. I will show you how to install the necessary Python code and the dependent libraries. I will show you how to download a video from YouTube with YT-DLP, how to cut certain parts of the video with LosslessCut, and how to extract the audio of a video with FFMPEG. I will show you how to do a transcription of a video or a sound. I will show you how to generate subtitles for any video. Finally, I will show you how to generate translated transcription and subtitles of any language video. With the translation feature of the Whisper model, you can watch any language (Whisper supports 99 languages) with English subtitles. Let's say you can find English subtitles for your favorite video in German or Japanese or Arabic. It is not a problem. Just follow my tutorial and generated English translated subtitles. Actually, to be precise, Whisper is able to transcribe speech to text in all the following languages, and therefore, translation of these following languages into English: {af,am,ar,as,az,ba,be,bg,bn,bo,br,bs,ca,cs,cy,da,de,el,en,es,et,eu,fa,fi,fo,fr,gl,gu,ha,haw,hi,hr,ht,hu,hy,id,is,it,iw,ja,jw,ka,kk,km,kn,ko,la,lb,ln,lo,lt,lv,mg,mi,mk,ml,mn,mr,ms,mt,my,ne,nl,nn,no,oc,pa,pl,ps,pt,ro,ru,sa,sd,si,sk,sl,sn,so,sq,sr,su,sv,sw,ta,te,tg,th,tk,tl,tr,tt,uk,ur,uz,vi,yi,yo,zh,Afrikaans,Albanian,Amharic,Arabic,Armenian,Assamese,Azerbaijani,Bashkir,Basque,Belarusian,Bengali,Bosnian,Breton,Bulgarian,Burmese,Castilian,Catalan,Chinese,Croatian,Czech,Danish,Dutch,English,Estonian,Faroese,Finnish,Flemish,French,Galician,Georgian,German,Greek,Gujarati,Haitian,Haitian Creole,Hausa,Hawaiian,Hebrew,Hindi,Hungarian,Icelandic,Indonesian,Italian,Japanese,Javanese,Kannada,Kazakh,Khmer,Korean,Lao,Latin,Latvian,Letzeburgesch,Lingala,Lithuanian,Luxembourgish,Macedonian,Malagasy,Malay,Malayalam,Maltese,Maori,Marathi,Moldavian,Moldovan,Mongolian,Myanmar,Nepali,Norwegian,Nynorsk,Occitan,Panjabi,Pashto,Persian,Polish,Portuguese,Punjabi,Pushto,Romanian,Russian,Sanskrit,Serbian,Shona,Sindhi,Sinhala,Sinhalese,Slovak,Slovenian,Somali,Spanish,Sundanese,Swahili,Swedish,Tagalog,Tajik,Tamil,Tatar,Telugu,Thai,Tibetan,Turkish,Turkmen,Ukrainian,Urdu,Uzbek,Valencian,Vietnamese,Welsh,Yiddish,Yoruba} The links and the commands I have shown in the video below: Open AI Whisper : https://openai.com/blog/whisper/ Whisper Code : https://github.com/openai/whisper Python : https://www.python.org/downloads/release/python-399/ Whisper install : pip install git+https://github.com/openai/whisper.git How to install CUDA support for using GPU when doing transcription of audio : First, delete existing Pytorch : pip3 uninstall torch Then install Pytorch with CUDA support : pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116 FFMPEG : https://github.com/BtbN/FFmpeg-Builds/releases LosslessCut : https://github.com/mifi/lossless-cut/releases How to extract sound of any video with FFMPEG : ffmpeg -i "test_video.webm" -q:a 0 -map a test_video.mp3 How to transcribe an English video : whisper "C:\speech to text\test_video.mp3" --language en --model base.en --device cpu --task transcribe How to transcribe an English video with CUDA support : whisper "C:\speech to text\test_video.mp3" --language en --model base.en --device cuda --task transcribe How to transcribe a Turkish video : whisper "C:\speech to text\test_video.mp3" --language tr --model base.en --device cpu --task transcribe How to transcribe a Turkish video with translation : whisper "C:\speech to text\test.mp3" --language tr --model small --device cuda -o "C:\speech to text" --task translate Our Discord for SECourses : https://discord.gg/rfttctFewW If you are interested in programming but you lack experience and skills I suggest you watch our playlists: https://www.youtube.com/c/SECourses/playlists [1] Introduction to Programming Full Course with C# playlist [2] Advanced Programming with C# Full Course Playlist [3] Object Oriented Programming Full Course with C# playlist [4] Asp.NET Core V5 - MVC Pattern - Bootstrap V5 - Responsive Web Programming with C# Full Course Playlist [5] Artificial Intelligence (AI) and Machine Learning (ML) Full Course with C# Examples playlist [6] Software Engineering Full Course playlist [7] Security of Information Systems Full Course playlist

✔️ If you are frustrated since #L2TP/PPTP is gone after MIUI 13 Update, or after your phone's / tablet's / device's Android Version update, then this full guide tutorial is for you. If your phone, tablet, or mobile device's Android version is above 11 and you can't find the #PPTP VPN protocol to connect your private #VPN, then don't worry. Because I am explaining the easiest way to set up our VPN to connect from your device in this tutorial guide. ✔️ Point-to-Point Tunneling Protocol (PPTP) was so easy to set up on Windows Server and you were able to connect your private VPN easily through your phone. But this is not possible anymore since PPTP is removed from the majority of phones and mobile devices. ✔️ So instead of setting up our private VPN through features of Windows Server, we are going to use open source #SoftEther VPN Project. ✔️ In this video I will show you thoroughly from scratch: 1: Generate a new virtual server on Hyper-V and install Windows Server 2019 evaluation version. 2: Install SoftEther VPN Project on Windows Server 2019. 3: Make the necessary configuration of SoftEther. 4: Generate and export the #OpenVPN configuration file. 5: Modify the OpenVPN configuration file which ends with the .ovpn extension. 6: Install the OpenVPN app through Google Play Market and import the .ovpn configuration. 7: Connect to your VPN from your phone. I demonstrate this with my Xiaomi Poco X3 Pro - Android 12 8: With this methodology, we don't have to deal with complex and very hard-to-set-up IKEv2 / #IPSec #MSCHAPv2, #IKEv2 / IPSec #PSK, and IKEv2 / IPSec #RSA VPN protocols. These are the only available protocols on my mobile device. 0:00 Introduction 1:17 New Virtual Machine 3:28 Setting up Windows Server 2019 7:20 SoftEther Download & Installation 11:56 How to Setup OpenVPN on the Phone and Use VPN ✔️ The reason why I made this video is, it was so hard and there wasn't any up-to-date guide/tutorial for setting up your private VPN and connecting from your mobile phone. ✔️ The subtitle of the video is manually corrected so please watch with subtitles. ✔️ Please join Our Discord server for asking questions and have discussions: 🔗 https://discord.gg/rfttctFewW ✔️ Please follow us on Twitter: 🔗 https://twitter.com/SECourses ✔️ Please follow us on Facebook: 🔗 https://www.facebook.com/OfficialSECourses ✔️ If you are interested in programming our playlists will teach you how to program and code from scratch: 🔗 https://www.youtube.com/c/SECourses/playlists 1️⃣ Introduction to Programming Full Course with C# playlist ⭐⭐⭐⭐⭐ 2️⃣ Advanced Programming with C# Full Course Playlist ⭐⭐⭐⭐⭐ 3️⃣ Object Oriented Programming Full Course with C# playlist ⭐⭐⭐⭐⭐ 4️⃣ Asp NETCore V5 - MVC Pattern - Bootstrap V5 - Responsive Web Programming with C# Full Course Playlist ⭐⭐⭐⭐⭐ 5️⃣ Artificial Intelligence (AI) and Machine Learning (ML) Full Course with C# Examples playlist ⭐⭐⭐⭐⭐ 6️⃣ Software Engineering Full Course playlist ⭐⭐⭐⭐⭐ 7️⃣ Security of Information Systems Full Course playlist ⭐⭐⭐⭐⭐ Thumbnail : freepik : Gradient vpn illustration