plenty of fast SSD storage space and pagefile set to 4x of RAM size minimum (if you can afford even large pagefile, go for it, but anything more than 128GB with 32GB of RAM is not necessary).Ĭooling - you will need to make sure your hardware is cooled adequately (check temps of your CPU and GPU during heavy use, if anything is above 85 degrees consider changign your CPU cooler and replacing thermal paste on both CPU and GPU if you know how to do it). 1000 series or higher Nvidia GPU with 8GB VRAM minimum (good for up to 256 resolutions models, for 256-320 11-12GB required, for 320+ 16-24GB GPUs required) 32GB of RAM for single GPU configuration, 64GB+ for 2+ GPUs modern 8-32 core CPU supporting AVX and SSE instructions plenty of storage space and pagefile set to 4 x of RAM size minimum. modern Nvidia or AMD GPU with 6GB of VRAM (good for up to 192 resolution models) modern 4 core CPU supporting AVX and SSE instructions Minimum requirements for making very basic and low quality/resolution deepfakes:
Make sure you use standard versions, using N/KN version of Windows may break DFL, if you use N/KN version of Windows install a free Media Feature Pack from Windows Store to ensure DFL works properly.
Windows 10 is generally recommended for most users but more advanced users may want to use Linux to get better performance. Usage of Deep Face Lab 2.0 requires high performance PC with modern GPU, ample RAM, storage and fast CPU. However, keep in mind that Google has banned making deepfakes on free accounts, you will have to buy a Colab Pro subsription or higher plan in order to use it (please check TOS before paying, I don't take responsibility if suddenly Google bans use of Colab DFL impletmentation also on paid plans).Ĭolab guide and link to original implementation: Ĭolab warning: Google is currectly trying to make use of DFL on Colab as hard as possible, you cannot use DFL on free Colab plans and users on Colab Pro are reporting issues too, it's possible soon Colab will completely ban use of any deepfake apps on their platform (most likely will create a dedicated, more expensive platform similar to Paperspace and other platforms desgined purely for training deep learning models and AI research).ĭFL paper (technical breakdown of the code): If you don't have a powerful PC with GPU with at least 8GB of VRAM you can use Google Colab's DFL implementation to train your models (but rest of the steps will have to be done locally).
DFL 2.0 DOWNLOAD (GITHUB, MEGA AND TORRENT): DOWNLOADĭFL 2.0 GITHUB PAGE (new updates, technical support and issues reporting): GITHUB IF YOU LEARNED SOMETHING USEFUL, CONSIDER A DONATION SO I CAN KEEP MAINTAINING THIS GUIDE, IT TOOK MANY HOURS TO WRITE. IF YOU WANT TO MAKE YOUR OWN GUIDE BASED ON MINE OR ARE REPOSTING IT, PLEASE CREDIT ME, DON'T STEAL IT. READ ENTIRE GUIDE, AS WELL AS FAQS AND USE THE SEARCH OPTION BEFORE YOU POST A NEW QUESTION OR CREATE A NEW THREAD ABOUT AN ISSUE YOU'RE EXPERIENCING!