Best Local Faceswap Tools for AMD GPUs in 2026

Running faceswap AI locally on AMD GPUs has historically been challenging due to the NVIDIA-centric nature of most AI tools. However, the landscape has improved significantly. ROCm maturity, DirectML support, and AMD-specific implementations now make local faceswap possible for Radeon users. This guide covers your best options and how to get them working.

The AMD AI ecosystem continues to improve, but honest assessment shows NVIDIA still has advantages. That said, if you have an AMD GPU, you can absolutely do local faceswap work.

Understanding AMD AI Options

Before exploring specific tools, understand the technology landscape.

ROCm (Linux)

ROCm (Radeon Open Compute) is AMD's equivalent to CUDA:

Supported GPUs:

• RX 6000 series (RDNA2)
• RX 7000 series (RDNA3)
• Some professional cards (MI series, W series)

Platform:

• Linux only (Ubuntu, RHEL primarily)
• No Windows ROCm support

Pros:

• Native AMD acceleration
• Growing ecosystem support
• Open-source

Cons:

• Linux only
• Installation can be complex
• Not all tools support it

DirectML (Windows/Linux)

DirectML is Microsoft's machine learning API that supports AMD GPUs:

Supported GPUs:

• Most modern AMD GPUs
• Works on Windows 10/11
• Linux support through WSL2

Pros:

• Windows native
• Broad GPU support
• Easier setup than ROCm

Cons:

• Performance overhead vs native
• Not all tools support it
• Some features may be limited

ONNX Runtime

ONNX Runtime with DirectML execution provider:

Pros:

• Cross-platform
• Good AMD support
• Many tools use ONNX models

Cons:

• Model conversion sometimes needed
• Performance varies

Different approaches enable AI on AMD GPUs

Top Faceswap Tools for AMD

Let's examine the best options for AMD users.

1. Rope (Recommended)

Rope has emerged as one of the most AMD-friendly faceswap tools.

AMD Support:

• DirectML backend for Windows
• ROCm support on Linux
• Active development for AMD compatibility

Features:

• Real-time preview
• Multiple face models (inswapper, etc.)
• Video processing
• Batch operations

Installation (Windows DirectML):

git clone https://github.com/Hillobar/Rope
cd Rope
pip install -r requirements.txt
python Rope.py --execution-provider dml

Performance:

• Functional on RX 6000/7000 series
• Expect 50-70% NVIDIA equivalent speed
• GPU memory usage similar to NVIDIA

2. roop-unleashed

A popular fork with extended features and AMD support.

AMD Support:

• DirectML execution provider
• ONNX-based models
• Windows and Linux compatible

Features:

• Multiple enhancement options
• Batch processing
• Various face detection models
• Quality presets

Installation:

git clone https://github.com/C0untFloyd/roop-unleashed
cd roop-unleashed
pip install -r requirements.txt
## Run with DirectML
python run.py --execution-provider dml

Performance:

• Reliable on modern AMD GPUs
• Face detection works well
• Swap quality comparable to NVIDIA version

3. FaceFusion

Modern faceswap tool with strong ONNX/DirectML support.

AMD Support:

• ONNX Runtime with DirectML
• CPU fallback available
• Active AMD compatibility work

Features:

• Clean interface
• Multiple face processors
• Streaming support
• Good documentation

Installation:

git clone https://github.com/facefusion/facefusion
cd facefusion
pip install -r requirements.txt
python facefusion.py --execution-providers dml

Performance:

• Good results on RX 6000+
• Some features may be slower
• Stable operation

4. DeepFaceLab (Limited)

The classic tool with some AMD considerations.

AMD Support:

• Limited DirectML builds exist
• Community AMD forks available
• Not officially supported

Features:

• Comprehensive training options
• High quality potential
• Large community

Challenges:

• Setup is complex for AMD
• May require specific builds
• Performance significantly reduced

ROCm Setup Guide (Linux)

For users willing to use Linux for best AMD performance.

Prerequisites

• Ubuntu 22.04 LTS (recommended)
• Supported AMD GPU (RX 6000/7000 series)
• Updated kernel (5.15+)

Installation Steps

## Add ROCm repository
wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | sudo apt-key add -
echo 'deb [arch=amd64] https://repo.radeon.com/rocm/apt/latest ubuntu main' | sudo tee /etc/apt/sources.list.d/rocm.list

## Update and install
sudo apt update
sudo apt install rocm-dkms

## Add user to groups
sudo usermod -a -G render,video $USER

## Reboot
sudo reboot

## Verify installation
rocm-smi

PyTorch with ROCm

## Install PyTorch with ROCm support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.6

Verify Setup

import torch
print(torch.cuda.is_available())  # Should return True on ROCm
print(torch.cuda.get_device_name(0))  # Should show your AMD GPU

ROCm setup enables native AMD acceleration

DirectML Setup Guide (Windows)

For Windows users, DirectML is the easier path.

Prerequisites

• Windows 10/11
• Modern AMD GPU with updated drivers
• Python 3.10+

ONNX Runtime DirectML

## Install ONNX Runtime with DirectML
pip install onnxruntime-directml

Verify Setup

import onnxruntime as ort
## Check available providers
print(ort.get_available_providers())
## Should include 'DmlExecutionProvider'

Running Tools with DirectML

Most tools accept an execution provider flag:

python tool.py --execution-provider dml
## or
python tool.py --execution-providers dml

Performance Expectations

Realistic performance assessment for AMD users.

Speed Comparison

Quality Comparison

Output quality is generally identical - the difference is in processing speed, not results.

Memory Usage

AMD GPUs often show similar or higher memory usage for equivalent tasks. Ensure you have at least 8GB VRAM for comfortable operation.

Troubleshooting

Common AMD-specific issues and solutions.

"No GPU detected"

Solutions:

• Update AMD drivers to latest
• Verify DirectML/ROCm installation
• Check execution provider flag
• Try CPU fallback to verify tool works

Slow Performance

Solutions:

• Ensure GPU is being used (not CPU)
• Update drivers
• Check thermal throttling
• Try different batch sizes

Crashes During Processing

Solutions:

• Reduce batch size
• Update ONNX Runtime
• Check for driver updates
• Monitor VRAM usage

ROCm Installation Fails

Solutions:

• Use supported Ubuntu version
• Check GPU compatibility list
• Install kernel headers
• Verify secure boot settings

CPU Alternative

When GPU acceleration fails, CPU processing remains an option.

When to Use CPU

• GPU not supported
• Driver issues
• Memory constraints
• Simple single-image tasks

CPU Performance

Expect 5-10% of GPU speed, but it works reliably on any system.

Setting CPU Backend

python tool.py --execution-provider cpu

Frequently Asked Questions

Can I use an RX 580 for faceswap?

Limited support. Older GCN cards may work with CPU or very limited DirectML support.

Is ROCm available on Windows?

No, ROCm is Linux-only. Use DirectML on Windows.

Which AMD GPU is best for AI work?

RX 7900 XTX offers best performance. RX 7800 XT is good value.

Why is AMD slower than NVIDIA?

CUDA ecosystem is more mature. Tools are optimized for NVIDIA first, AMD support comes later.

Can I use WSL2 for ROCm?

Limited support exists but native Linux is more reliable.

Is the quality worse on AMD?

No, output quality is identical. Only processing speed differs.

Do I need special drivers?

Latest AMD Adrenalin drivers. No special AI drivers needed for DirectML.

Can I train models on AMD?

Possible with ROCm on Linux, but faceswap tools mostly use pre-trained models.

What about integrated AMD graphics?

Not recommended. Dedicated GPU required for reasonable performance.

Is cloud GPU better than local AMD?

For heavy workloads, cloud NVIDIA may be faster. For occasional use, local AMD is more convenient.

While AMD GPUs require more setup effort than NVIDIA equivalents, functional local faceswap is achievable. The DirectML ecosystem continues to improve, and tools are increasingly supporting AMD out of the box.

For users who want to skip local setup complexity, Apatero offers faceswap capabilities through their hosted platform, eliminating GPU compatibility concerns entirely.