![]() Follow the steps 1-3 from Installing without Git ⚠️ ** Fallback** ⚠️ □️ Page Index for this GitHub Wiki You will need to download the latest full project's zip file and extract it. If you did not encounter any errors, congratulations, you are done! :) Updating if you downloaded the zip and did not use Git open Command Prompt and navigate to long_DFL_directory_name\_internal\DeepFaceLab.The easiest way to update is via the git pull command. double click python_console.bat, after which you should see something like this.rename extracted directory from DeepFaceLab-master into DeepFaceLab.The file offered for download should be called DeepFaceLab-master.zip Please note: you will not be able to use Git to update this kind of install.īefore going over the steps to set everything up, first go to the MVE-DFL fork's project page and then click Code > Download ZIP as in this picture: type and run python -m pip install -r requirements-cuda.txtĪfter everything is done you should be able to see a new DeepFaceLab directory inside your _internal, something like this:Īnd if you did not encounter any errors, congratulations, you are done! :). ![]() after it is done, type and run cd DeepFaceLab.open Command Prompt (Win+R > cmd) and navigate to your DFL\_internal directory, something like this:.either rename DeepFaceLab, for example into DeepFaceLab_old so you keep a copy if you run into problems (recommended) or delete it.go to your existing DFL directory and open _internal, you should see something like this:.This is the recommended option, as it will make updating easier as well. If you already have DFL installed and set up, there's only a few steps to do to start using this fork. Installing the MVE-DFL fork with an existing DFL installation You can now continue to Installing the MVE-DFL fork with existing DFL installation. Inside your DFL directory you should find:ĭepending on which build you downloaded and extracted, your DFL directory will look something like this: It is also recommended you put DFL on an SSD, if possible It is recommended you avoid any spaces in the path to the directory where you extract DFL Secondly, run the downloaded executable file, or use your archive application of choice (ie 7-zip) to extract the content. This includes AMD Radeon R5, R7, R9 200 and newer, Intel HD Graphics 500 and newer, and some older Nvidia GPUsĬUDA-based builds will provide better performance than DX12 build DeepFaceLab DirectX 12 build - for AMD, Intel, or Nvidia GPUs that fully support DX12.DeepFaceLab NVIDIA up to RTX 2080 TI build - for Nvidia GPUs that support CUDA, and are not from series 3000.DeepFaceLab NVIDIA RTX 3000 series build - for Nvidia 3000 series GPUs (uses CUDA).Make sure to download the build appropriate for your hardware: bat files.įirst visit, and use one of the available options (torrent, or direct download from Mega.nz or ) to download DFL. You can download them from here: DeepFaceLab-bat_files.zipĪnd then simply extract them in your main DFL directory, next to your existing. bat files that make it easier to use some of the new functionality of this fork. It is recommended you download and extract the additional. You do not need to know (almost) anything about Git itself, it will be enough to copy the commands from this article. If you don't already have it, it is recommended you install Git for Windows. Installing and updating the MVE-DFL fork is easiest via Git. While it is possible to run DFL on Linux, that will not be covered in this article. Click OK and the torrent will automatically try connecting to the new trackers.Please note: This article is written with Windows 10/11 in mind. You need a blank line in between each tracker that you enter. Paste the list that you copied into the box.Right-click the torrent in your client, select Properties > General to find the list of trackers.Find a list of active trackers online and copy the list to your clipboard. ![]() Do not attempt this if you are using a private tracker, as you may get banned. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |