PC deepfake tools ranked by how much runs on your own machine

If privacy is the deciding factor, install DeepFaceLab or Faceswap.dev: both train deep-learning models entirely on your PC and never touch a server. Want results without writing a line of script? HitPaw FotorPea does offline swaps for Windows and Mac. No capable GPU at all? Google Colab (Roop) is the free escape hatch, but it runs in the cloud, so your face data leaves the building.

Everything below is ordered by one axis only: how completely the work happens locally. Fully offline trainers sit at the top, no-code desktop apps in the middle, cloud notebooks last.

Best deepfake software for PC at a glance

Fully offline winner: DeepFaceLab. Local model training, free and open source, no watermark, but script-based and GPU-hungry.
Best for beginners who still want local processing: HitPaw FotorPea. Runs offline on Windows and Mac; the free version stamps a watermark.
Best free option without a strong GPU: Google Colab (Roop). Cloud-based, so not local, and the notebooks break often.

Two of these three cost nothing. The catch is rarely the price tag. With DeepFaceLab you pay in hours and hardware. With HitPaw the free tier blends a watermark into your export. With Colab you pay in privacy, because the swap algorithm runs on Google's machines, not yours.

How we ranked: offline-first, hardware-aware

Rank position here tracks one thing: the share of the pipeline that stays on your computer. Fully offline desktop trainers (DeepFaceLab, Faceswap.dev) lead, no-code desktop apps follow, and cloud or Colab tools land at the bottom because face data has to be uploaded to run.

To confirm a tool is genuinely local, the HitPaw team ran each one with the Wi-Fi disabled and watched whether it still worked. A swap that completes with no connection cannot be quietly shipping your images to a server. That is the offline proof we trust over any marketing claim, and it is easy to repeat yourself before feeding a tool sensitive photos.

The benchmark figures cited throughout come from a documented test run: HitPaw reports over 20 hours spent across these tools on a mid-range Windows PC with an RTX 3060 GPU and 16GB of RAM. Keep that rig in mind. It sets a realistic floor, not a high-end ceiling.

Fully offline, pro-grade: DeepFaceLab and Faceswap.dev

These two are the reason the list starts here. Both train a model on your own frames, both run with no internet, and both ask for real hardware and patience in return.

DeepFaceLab: the quality benchmark with a steep cost

DeepFaceLab is, by its own GitHub description, the leading software for creating deepfakes, training your own model on Windows with GPU support. There is no make-everything-ok button. You study the workflow or you get nowhere. Fritz AI puts it plainly: very high-quality output, large community, but not for beginners, no user interface, and heavy demands on processing power and storage with no real-time support.

One thing to know before you commit. The DeepFaceLab repository was archived read-only on Nov 13, 2024, so active development has stopped. The code and its community remain, with 19.1k stars, 850 forks, and 1,317 commits on GitHub, which tells you how many people have leaned on it. But you are adopting a frozen project, not a living one.

A desktop monitor filling the frame, displaying the DeepFaceLab command-line workflow with a column of numbered batch scripts and a half-finished face-training preview window beside it. The scene sits on a dim home-office desk with a graphics card box and coffee mug just visible at the edges. Cool blue monitor glow falls across the keyboard from the front, harsh against the warm desk lamp behind, throwing soft shadows toward the viewer. Focused, slightly intimidating late-night working mood.

Faceswap.dev: the more approachable offline trainer

Faceswap.dev does the same fundamental job with less friction. It uses deep-learning models for realistic swaps, is fully compatible with Windows, and runs without internet, so nothing uploads. HitPaw notes the one condition: you need a powerful GPU to process swaps quickly. Fritz AI adds that it is open-source and cross-platform with a modular plugin architecture, more approachable than DeepFaceLab, though it still wants some dev skill and good hardware. Pick this when you want local training but not the bare script grind.

Worth flagging for the live-video crowd: DeepFaceLive is the real-time offline counterpart to DeepFaceLab, and community forks like Roop Unleashed, FaceFusion, and Wunjo AI keep one-click local swapping alive. FaceFusion in particular tends to struggle once a clip runs past a few seconds, so test it on your actual footage length before committing to it.

Offline but beginner-friendly desktop apps

This tier is the privacy and ease sweet spot: installed apps that process on your machine yet ask for zero code.

HitPaw FotorPea is the fastest of them. It runs as an offline AI face swap for Windows and Mac, and in testing it processed a high-res photo in under 3 seconds, with Auto-Correction handling a side-profile shot well. That last detail matters, because side profiles are exactly where cheaper tools fall apart. The trade-off is small but real: the free version adds a watermark to your output.

Already editing a video and need just one shot swapped? Wondershare Filmora fits there. Its Replace Element tool lets you select a specific region to swap and guide the blending with a text prompt, all inside the editor. It is a general video editor rather than a dedicated trainer, so you trade deep model control for the convenience of doing the swap without leaving your timeline.

Deepfake Maker rounds out the group. It is a desktop app for Mac and Windows that swaps faces across photos, GIFs, and videos using GANs, automating face detection, training, and the swap itself. Public detail on its exact hardware needs is thin, so treat output quality as something to verify on your own clips rather than assume.

Free, no-GPU route: Google Colab (Roop)

No capable graphics card? This is your way in. Google Colab running Roop executes the deepfake algorithm in the cloud, so anyone with a Google account can produce a swap without high-end hardware, as Resemble AI documents. Free, and no GPU bill.

But read why it sits last. The processing happens on Google's servers, not your PC, which means your face images do leave your machine. That is the exact privacy trade-off the offline tools exist to avoid. And reliability is shaky. Notebooks break, get restricted, or simply stop finishing jobs.

The errors are predictable enough to name. A [WinError 2] with "ffmpeg not installed" stops video swaps because the notebook cannot find the tool that stitches frames back into a clip; installing or pointing to ffmpeg clears it. CUDA call failures usually mean the runtime lost its assigned GPU. Jobs that spin forever and never complete are often a quota cap rather than your mistake. Knowing the cause saves you from re-running the same broken cell ten times.

Hardware reality: what GPU you actually need

Most roundups skip this part, which is why first renders disappoint. The offline pro tools, DeepFaceLab above all, need a powerful GPU plus significant time and storage, and they offer no real-time shortcut. A model trains for a long stretch before a single convincing frame appears.

Anchor your expectations to the documented test rig: an RTX 3060 with 16GB of RAM. That mid-range card produces results, but a short clip that crawls through training on a 3060 will move dramatically faster on something like an RTX 4090. The card sets your render and training time more than any setting in the software does. Even owners of an RTX 4080 or 4090 report blurry output or repeated failures, so raw power alone does not guarantee a clean swap.

Why does realism break? A face model learns from the frames you feed it, and most training footage is close to front-on. Turn the head into a side profile, add fast motion, or change the lighting, and the model has little reference to draw from, so the face flickers, smears, or jumps between frames. Feed it varied angles up front and these artifacts shrink.

Legal and consent before you share

The tool is the easy part. What you do with the output carries the risk. Always seek consent before using someone's likeness or voice, as Resemble AI stresses, and think about privacy before you share anything.

The legal line is clearer than people assume. Creating deepfake content for entertainment or artistic purposes is generally legal; using the technology to deceive or defraud is not. That single distinction covers most of what a hobbyist needs to know.

Choosing a local tool helps on the privacy side too. Offline desktop apps keep processing on your machine, so there is no need to upload sensitive images, which directly reduces data-exposure risk. It is one more reason the offline picks earn the top spots here.

FAQ

Which deepfake tools run fully offline on Windows?

DeepFaceLab and Faceswap.dev both train and swap entirely on your PC with no internet connection. HitPaw FotorPea also runs offline for no-code users. Confirm it yourself by disabling Wi-Fi and checking the swap still completes.

Is deepfake software free?

Several are. DeepFaceLab and Faceswap.dev are free and open source. Google Colab (Roop) is free to run. HitPaw FotorPea has a free tier, but it adds a watermark, and "free" cloud tools can hide credit costs or usage caps, so check before you rely on one.

What hardware do you need to run DeepFaceLab?

A powerful GPU, plenty of storage, and patience for long training. The documented reference rig is an RTX 3060 with 16GB of RAM, which works but is the practical floor. A stronger card cuts render and training time sharply.

Is using deepfake software legal?

For entertainment or artistic use, generally yes. Using it to deceive or defraud is not legal. Get consent before using anyone's likeness or voice, regardless of your intent.