Real Time Webcam DeepFake Face Swapping with Rope Pearl Live 1 Click Install and Use Fast and Easy
Full tutorial link > https://www.youtube.com/watch?v=whDt36YwEKQ
0-shot most advanced Deepfake / Face Swapping application Rope Pearl now supports TensorRT and real-time webcam processing. In this video, I will show how you can 1-click install Rope Pearl Live into your computer and use webcam Deepfake feature. The installer will do entire installation automatically for you and I will show how to use this amazing new version.
#rope #deepfake #faceswap
🔗 Rope Pearl Live Installers Scripts
🔗 Requirements Step by Step Tutorial
🔗 Main Windows Tutorial
🔗 Cloud Massed Compute Tutorial (Mac users can follow this tutorial)
🔗 Official Rope Pearl Live GitHub Repository
🔗 SECourses Discord Channel to Get Full Support
🔗 Our GitHub Repository
🔗 Our Reddit
00:00:00 Introduction to the Rope Pearl real time live face swapper
00:01:20 How to download and install Rope Pearl live on your Windows computer
00:05:21 How to verify installation and save the logs
00:05:51 How to start and use the Rope Pearl live after installation has been completed
00:06:29 How to set parameters and swap face
00:07:38 How to save processed - faces changed video
00:08:24 Rope Pearl processing speed with CUDA on RTX 3090 TI
00:08:41 How to install TensorRT and use it to speed up significantly
00:10:34 How to manually add TensorRT libraries to the system environment variables Path
00:11:10 The real time processing speed of TensorRT
00:12:13 How much VRAM TensorRT uses
00:12:56 How to use your webcam to real-time swap faces and use the swapped face having webcam output video
Inswapper and Deepfakes: The Evolution of Synthetic Media
In recent years, the realm of artificial intelligence and computer vision has seen remarkable advancements, leading to the development of increasingly sophisticated technologies for manipulating and synthesizing media. Two prominent examples of these technologies are Inswapper and deepfakes. This article will explore these concepts in detail, discussing their origins, technological underpinnings, applications, and the ethical concerns they raise.
Deepfakes: The Foundation
Deepfakes, a portmanteau of "deep learning" and "fake," refer to synthetic media in which a person's likeness is replaced with someone else's in existing images or videos. This technology emerged in late 2017 when an anonymous Reddit user called "deepfakes" began sharing manipulated pornographic videos featuring celebrity faces seamlessly swapped onto the bodies of adult film actors.
The technology behind deepfakes relies on deep learning algorithms, particularly generative adversarial networks (GANs). GANs consist of two neural networks: a generator that creates fake images, and a discriminator that attempts to distinguish between real and fake images. Through an iterative process, the generator improves its ability to create convincing fakes, while the discriminator becomes better at detecting them.
Inswapper: A Specialized Tool
Inswapper, short for "face inswapping," is a more recent and specialized tool within the broader category of deepfake technologies. Developed by ArcFace, Inswapper focuses specifically on face swapping in images and videos. It utilizes advanced machine learning techniques to achieve highly realistic face replacements with minimal input data.
Key features of Inswapper include:
Efficiency: Inswapper can produce high-quality face swaps with a single reference image, unlike many deepfake algorithms that require extensive training data.
Preservation of expressions: 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.
Improved 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 use 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.
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00:00:00 Greetings everyone. In this tutorial, I will show you how you can real-time face swap your webcam
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00:00:07 with any face you want by using the most advanced face swapping easy zero-shot deep fake application
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00:00:15 Rope Pearl. If you remember the Rope Pearl application, I have made two tutorials for it. So,
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00:00:22 in this tutorial, you will see how to use this amazing application on your Windows computer and
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00:00:27 do real-time face swapping by using the TensorRT model with InSwapper with 512 restoring faces and
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00:00:37 other features. It will be a one-click install and use on your computer very fast with TensorRT.
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00:00:44 Since I am recording a video with OBS Studio, it is not as fast as it would be without recording
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00:00:51 a video. So, let's begin. As usual, I have prepared detailed instructions and a one-click
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00:00:57 installers. Everything is in this post. The link to this post will be in the description of the
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00:01:03 video. If you don't know how to use this amazing Rope application, first of all, watch our main
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00:01:09 Windows tutorial. And if you are going to use it on a cloud, if you don't have a powerful GPU,
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00:01:14 watch our Massed Compute cloud tutorial first. Then you can watch this tutorial. When you go
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00:01:21 to the very bottom of the post, you will see 3 attachment files: Rope version 4, Rope landmarks,
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00:01:28 and Rope Live stream. Now you may be wondering, what does this mean? Rope version 4 is the
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00:01:34 original main development of the Rope application. Rope landmarks is the version, a fork of the main
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00:01:41 repository developed by Alucard, and Rope Live stream is the latest fork of the Rope application
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00:01:49 developed by argenspin. So, this is the newest version that supports all of the previous
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00:01:55 versions, all of the features that Alucard has or the main repo has, and additionally, this
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00:02:02 is the fork that has the TensorRT and also webcam feature. So I suggest you use the Rope Live stream
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00:02:10 version. You see you can click here to download but I suggest you download it from attachments
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00:02:15 because sometimes there may be mislinking or I may not put the link into the description so the
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00:02:21 attachments is the accurate part where you will download. Okay, after you downloaded the Rope Live
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00:02:27 stream version 4 zip file, go to the downloads and cut it and paste it into any hard drive that
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00:02:35 you want to install. Let's install it into our F drive. Do not install it into your users' folder,
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00:02:41 into the Windows folder, or into cloud folders like OneDrive. So extract here,
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00:02:47 enter inside the extracted folder. Do not have special characters in your folder path or do not
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00:02:54 have space characters in your folder path because it may break the application. Then all you need
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00:02:59 to do is double-click windows install.bat file. This will install the application fully for you.
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00:03:06 It will also download all of the necessary models, which are over 20 different models,
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00:03:11 so this will make everything for you. But for this application to work, you need to have
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00:03:17 several installations. You need to have Python 3.10, you need to have Git and FFmpeg, CUDA 11.8,
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00:03:25 this is super important, and C++ tools. If you don't know how to install them, I already have a
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00:03:31 tutorial for that, an amazing tutorial. Let's see that. So click here to see it. This is a 34-minute
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00:03:37 tutorial and in this tutorial, you will see all the video chapters. So by following this tutorial,
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00:03:43 you can install all the requirements. When installing Python, please install it into
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00:03:48 your C drive directly as I have shown. It is super important. Once you do this installation,
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00:03:53 you will be able to run all of the newest AI applications without ever having any issues.
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00:04:00 This is a public tutorial on our channel. So even if you are not my Patreon supporter, you can still
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00:04:04 watch this tutorial to learn how to install the primary elements of any AI application. So the
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00:04:11 installation is first cloning the repository. It's a big one with a lot of files over 200 megabytes.
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00:04:18 If you encounter any problems related to your internet connection, you can use WarpVPN. It's
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00:04:23 a free VPN. Just type WarpVPN. This is provided by Cloudflare and it should fix your issues with
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00:04:31 downloading when you are using the installers. Moreover, all of my installers installed into
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00:04:38 their virtual environment. So they will not affect any other installation on your system.
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00:04:44 They will be completely isolated. Moreover, you can look at the content of every file that
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00:04:49 I have. Everything is fully transparent. There is nothing precompiled or encrypted. The installation
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00:04:55 of the virtual environment is completed. Now it is downloading the necessary models automatically for
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00:05:00 us. The downloader script that I have made will not re-download if already completely
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00:05:06 downloaded or it will resume if the download was interrupted. So it's a very robust script that you
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00:05:12 can use for other applications and purposes as well. All right. The installation is completed.
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00:05:17 You see the virtual environment made and installed properly. But before. If you are using any key to
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00:05:23 close this, scroll up and verify that everything is accurately installed. There are no errors.
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00:05:29 Moreover, right-click here, edit, select all, then right-click, edit, copy, and save all of
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00:05:36 the installation logs because if there are any errors, you need to email me or message me from
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00:05:42 Patreon so I can check and see what the error you have. So let's press any key to close this. Let's
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00:05:48 go back to our installation folder, which is here. Let's enter inside Rope Live. Let's check out the
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00:05:53 model's total size, which is 5 gigabytes. And before installing the TensorRT, let's start our
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00:06:01 application. To start, windows start.bat file. It will start the Rope application. Let's make full
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00:06:07 screen. Okay. I will turn off my webcam so you can see this area as well. So to use this application,
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00:06:14 watch the Windows tutorial to learn how to use it, but I will show quickly. Let's select the videos
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00:06:18 folder. I already have included some links. Let's click on the videos folder. I already
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00:06:21 have included some links. I already have included some links. Let's click on demo. Images and faces,
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00:06:23 which will be automatically downloaded. Let's open them. Let's also select our save folder into here.
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00:06:29 And we are ready to testing. So let's select the video. Let's click find faces. Let's change the
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00:06:35 Brad Pitt face into, for example, this one. So it is not changed yet. Before starting changing,
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00:06:43 let's set up the parameters. So I'm going to use InSwapper, which is the best. We are going
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00:06:48 to use CUDA because we didn't install TensorRT yet. You see there is also webcam resolution,
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00:06:52 which I will show you when I am showing the webcam. Currently, I will not show it. I will
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00:06:57 show it with the TensorRT, but it is not mandatory to use TensorRT. Okay. Let's also enable Restorer
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00:07:03 with GFPGAN. You can also play with this. I have shown everything in the Windows tutorial.
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00:07:09 And this is very important. Setting up the swap resolution 512. Then click swap faces and just
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00:07:17 wait. If there is any error, you will see it on the CMD. So the CMD window is very important to
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00:07:24 see the errors. The interface will freeze whenever it is loading, whenever it is doing something.
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00:07:29 Okay. You see the face has been changed. Now we can click this play icon and it will start
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00:07:35 playing the video with changed faces. It is not saving yet. For saving, you need to click this
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00:07:41 record button to save into the output folder. The speed will depend on your GPU. The VRAM usage is
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00:07:48 like this. If your VRAM is not sufficient, you can reduce the thread count which is set here.
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00:07:54 You see this is the thread count. Let's make it like 3 for example. So after doing this change,
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00:07:59 you may need to clear VRAM. Then click play and it will reduce the number of threads. So it will
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00:08:05 use a lesser amount of VRAM. Don't forget to change the thread count from here if you have
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00:08:12 a thread problem. Okay. So now let's install the TensorRT and see the speed. But before doing that,
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00:08:18 let me show you the 5 thread count speed one more time. So let's click play. So it is playing with
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00:08:24 this speed. You can see the frames numbers from here. So these are the processed frames right now.
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00:08:29 So this is the speed of RTX 3090 Ti with thread count 5. Okay. So let's close the application.
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00:08:37 So you see it is also terminated. Let's click here. Let's return back to our folder. Now all
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00:08:41 you need to do is double-click Windows install TensorRT step 2. This will download the TensorRT
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00:08:49 files automatically for you. It will extract into this folder and add it to the environment
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00:08:55 path automatically for you. So it should work out of the box automatically for you. So the TensorRT
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00:09:02 installation is also completed. Let's check out the logs. It is looking accurate. There are no
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00:09:07 errors or issues. Then press any key to continue. Now let's start back our application again by
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00:09:14 clicking the start icon. It has started. Once you started the application, click here. It will
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00:09:20 refresh the folders like this. You see currently refreshing. Okay, the folders have arrived. Then
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00:09:26 click find faces. Select our face. Select swapping face. Let's select TensorRT. Enable Restorer.
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00:09:33 Change the swapper resolution to 512. And then click swap faces. Now after you click swap faces,
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00:09:41 when you first time running a configuration, it will generate a TensorRT model. However, it is not
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00:09:47 displayed on the CMD window. So you just need to wait patiently until it is completed. Let's look
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00:09:55 at the task manager. And we should see that our GPU is being used. You see it is being used right
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00:10:00 now to compile the TensorRT model. Whenever you make some serious changes in the configuration,
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00:10:06 it may recompile the TensorRT model. I think it depends on the Restorer model and the used face
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00:10:14 swapping model and some other changes that affect the TensorRT model. So you see. Just wait until
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00:10:20 it is completed. It doesn't take much on my RTX 3090 Ti. But it depends on your GPU. Just wait
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00:10:27 until this screen freeze has ended. Currently, you see the Rope screen is frozen. Meanwhile,
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00:10:34 let me show you which path parameter added to the system variables. This is important because
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00:10:39 if it fails on your computer, you need to add it manually. But it should be automaticly. So open the
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00:10:45 path from here. And in here, you see that the downloaded TensorRT. library added. You will
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00:10:51 see that I already have another one which was from my previous installation. But this is how
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00:10:56 you add your downloaded TensorRT files into your path variable. Okay. The TensorRT model has been
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00:11:04 generated. And now you see that the face has been changed. The interface is unfrozen. Now
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00:11:10 let's see the real-time speed. So let's play and see. Okay. Currently, it is working with TensorRT.
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00:11:18 And. I can say that yes. It is really really faster than before. We have Restorer enabled.
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00:11:24 We have GFPGAN enabled. We are using right now. Also, we are using the 512 model. And it
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00:11:31 is really really fast right now. Fast playing. Okay. Let's also increase the thread count and
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00:11:38 see the effect of the TensorRT model. So I'm going to make this 10. Then hit enter. So the
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00:11:45 slider will be applied. Then let's go to the very beginning. Click record and click play. And let's
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00:11:52 see the real-time speed. By the way, I am right now recording the video which is a 4K video. Also,
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00:11:59 Nvidia broadcast is running. So I have a lot of backend running right now. But the speed is
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00:12:05 incredible you see. It is really really fast while I am doing all of this. VRAM usage is
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00:12:11 very very minimal right now. So it is using like 5 gigabytes total. Probably lesser than that because
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00:12:17 there are other applications running at the back. So it is working amazingly with TensorRT. This
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00:12:23 application is now blazing fast. Once you enabled recording you will see that the video saved as
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00:12:29 like this. So total time was 56 seconds. And the duration of the video. Let's open and see again.
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00:12:36 So the video is saved here. You see test video. Let's open it. So the duration of the video is
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00:12:42 21 seconds. So 21 seconds duration video which is full HD. 1920 to 1080. Saved in 55 seconds.
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00:12:54 Almost real-time. Not real-time but almost. Now I will show you how you can use the webcam feature
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00:12:59 to real-time replace your face. To do that I need to close my OBS first. Then start the webcam here.
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00:13:09 Then I will record. But to be able to do that you need to have OBS virtual cam. Currently this
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00:13:15 is my OBS screen. So you need to. Click start virtual cam. Then stop virtual cam. And exit
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00:13:21 from OBS to be able to start your webcam in your Rope application. Don't forget that. Click start
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00:13:29 virtual cam. Then stop virtual cam. Exit your OBS. Open your Rope application. Start webcam.
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00:13:35 I will show you how to continue after this. Since this is important I will show you this as well.
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00:13:41 You see after I stopped my OBS and clicked start Rope. So that Rope can see my webcam. You see my
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00:13:49 Kaspersky antivirus warned me about this. So if you have an antivirus it may block your webcam
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00:13:56 usage on the Rope application. I will allow. So it will be allowed to use my webcam. However since
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00:14:03 OBS is running right now I can't get the webcam. So I will stop the OBS one more time. Okay so my
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00:14:08 webcam appeared here. I clicked it. Then I clicked the play button. Now my webcam is displayed on the
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00:14:14 Rope interface. I started my OBS as well. First of all before starting webcam and live webcam
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00:14:20 testing. Make sure that you enabled send frames to virtual camera here. So you can reduce your webcam
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00:14:27 resolution from here to get a maybe more smoother output. Maybe more accurate-looking output. So you
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00:14:34 can change your webcam resolution from here. You can change your webcam fps from here. Now
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00:14:40 what I am going to do is enable this again. It was disabled once I changed the resolution. So
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00:14:45 now we are ready to do live changing. To do that look at the camera and click find faces. It will
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00:14:52 get your face like this. Then you can reduce your similarity threshold to follow your face
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00:14:58 very accurately. From here like this. Or maybe like this. Then select the face that you want to
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00:15:04 change real-time. I don't have a compatible face with myself. I didn't look for it. So the quality
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00:15:10 will not be the best. However, it should be enough to show you. So I will use this face. Then
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00:15:17 click swap faces. Then we are going to get a live swapping like this. Okay. Let's see currently this
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00:15:25 is live. On Rope application it is not very well looking. But now I will show you on the virtual
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00:15:33 camera. Moreover, you can reduce your webcam resolution. You can reduce your fps if your GPU
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00:15:39 is not able to keep up with it. You can play with other parameters to get more accurate results.
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00:15:45 But. This is just for showing you. So I will say webcam test like this. Let's open a webpage. So
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00:15:54 you can use this on Google meeting wherever you are using. And from here I will select
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00:15:59 OBS virtual camera. Now this is important. You select OBS virtual camera for a live test. Test
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00:16:05 my cam. You see Google Chrome using. And now it is live. Okay. There is some delay right now with
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00:16:10 my configuration. And also I am recording a 4K tutorial right now. So it is also asing a lot of
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00:16:17 CPU, GPU power. And reducing some fps. And when I change my face fully. It may get broken. But yeah.
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00:16:24 You see. It is working pretty decen actually. So it is up to you. And we may probably reduce the
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00:16:31 webcam resolution further more. Decrease it more. But this is how you can use Rope Pearl real-time.
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00:16:38 So this is real-time usage of Rope Pearl. Now there is nothing else left for this tutorial to
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00:16:45 show. If you are going to use this application on a cloud. The Massed Compute tutorial is fully up
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00:16:50 to date. Just follow this tutorial to install on Massed Compute. And for using live webcam.
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00:16:56 I don't know how you can connect a webcam to the virtual computer. But the application will
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00:17:01 work perfectly on there. So you can swap existing videos on Massed Compute very fast. Just follow
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00:17:08 this tutorial. The installer scripts are already included in the zip file. Currently on Massed
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00:17:13 Compute, on Ubuntu, TensorRT is working. I couldn't make it work yet. But CUDA is working
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00:17:19 very well. You know when we don't have TensorRT. We use CUDA with Nvidia GPU. And if you don't have
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00:17:25 any Nvidia GPU. You can use CPU. But it will be super slow. The CPU will be super slow. So this
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00:17:31 is how you use it. You can always message me on Patreon. Also join our Discord channel. And ask
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00:17:37 me any questions that you want. We also have now Reddit. Subreddit. You can join our Reddit
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00:17:42 subreddit. I will share a lot of useful stuff here. And to find our Discord channel. Just click
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00:17:49 this link. You can also type this into Google to join our Discord channel. You see software
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00:17:53 engineering courses, SEcourses. We have over 7000 members. I reply to every message on Discord.
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00:18:00 Every email. Every Patreon. Comment. Everything. I am fully dedicating myself to you guys. Thank
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00:18:07 you so much for watching. Sharing. Liking. Please also star our GitHub repository. You see when you
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00:18:12 click here. You will go to our GitHub repository. Star it. Fork it. Watch it. If you sponsor me. I
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00:18:17 appreciate that. So hopefully see you in another next amazing tutorial video. Bye.