How to Create AI Videos Locally on Your AMD GPU: A Complete Guide
Z
Zack Saadioui
8/12/2025
So You've Got an AMD Card & You Want to Make AI Videos? Let's Talk.
Hey everyone. Let's get real for a second. If you're into local AI stuff—image generation, and ESPECIALLY the newer image-to-video models—and you're rocking an AMD graphics card, you've probably felt a little left out of the party. It seems like every new, cool tool or tutorial is tailor-made for NVIDIA, with "just run this CUDA command" sprinkled everywhere. It can be SERIOUSLY frustrating.
Honestly, for a while, the advice was just "sell your AMD card & buy NVIDIA." And yeah, that's one way to go, but it's not the only way. The truth is, your AMD GPU is a beast. That 7900 XTX or even a trusty 6700 XT has a ton of power, and there's absolutely no reason it should be sitting on the sidelines. The hardware isn't the problem; it's always been about the software.
But here's the good news: things are changing. FAST. Thanks to a ton of work from AMD & the open-source community, running high-end image-to-video models locally on your Radeon card is not just possible, it's getting pretty darn good. It's still a bit more of a "roll up your sleeves" process than the plug-and-play world of NVIDIA, but if you're willing to get your hands a little dirty, you can unlock some incredible creative potential.
This is going to be a deep dive. We're going to cover the why, the how, & the what. Why has it been so tough for AMD users? How do you actually set up your system to do this stuff? And what tools & models should you be looking at? Let's get into it.
The Elephant in the Room: CUDA vs. ROCm
To understand why this has been a challenge, you need to know about two key pieces of technology: CUDA & ROCm.
CUDA (Compute Unified Device Architecture) is NVIDIA's baby. It's a proprietary platform that lets software developers tap directly into the power of NVIDIA GPUs for all sorts of complex calculations. Because NVIDIA got a head start & invested heavily in it, CUDA became the de facto standard for the entire AI & machine learning world. Almost every major AI framework, from TensorFlow to PyTorch, was built with CUDA support from day one.
ROCm (Radeon Open Compute platform) is AMD's answer. It's an open-source software stack designed to do the same thing: let developers use the immense parallel processing power of AMD GPUs for AI & high-performance computing (HPC). The key word there is "open-source," which is awesome, but it also means it's taken time for the community & AMD to build up the same level of support & polish that CUDA has enjoyed for years.
For a long time, if a new AI model came out, it was CUDA-first. Getting it to work on ROCm was an afterthought, if it happened at all. But that's where the tide is turning. ROCm has matured a LOT. Major frameworks like PyTorch now have official ROCm support, & the community has been hard at work creating ROCm-compatible versions of all the best tools.
So, the core of this guide is this: to do serious AI video work on your AMD card, you need to embrace the ROCm ecosystem.
Your New Best Friend: ComfyUI
If you've dipped your toes into AI image generation, you've probably heard of Automatic1111's Stable Diffusion Web UI. It's fantastic, but honestly, getting it to work perfectly on AMD can be a bit of a hack. People often use workarounds like ZLUDA (a kind of translation layer that makes your AMD card pretend to be an NVIDIA card) or Microsoft's DirectML. These can work, but they can also be slower & not support every feature.
Instead, I'm going to point you towards ComfyUI.
ComfyUI is a different kind of interface for Stable Diffusion & other generative models. It’s a node-based system, which looks a bit like a flowchart. At first, it can seem more intimidating than a simple web form, but its flexibility is EXACTLY what makes it so powerful for AMD users.
Here’s why ComfyUI is the way to go:
Granular Control: Because every part of the generation process is a separate node (load checkpoint, load VAE, prompt encoder, sampler, etc.), you have total control. This makes it easier to troubleshoot & adapt workflows for specific hardware.
ROCm First: Many of the most dedicated AMD AI users have flocked to ComfyUI, so a lot of the development & testing for ROCm compatibility happens here first. You're more likely to find workflows & custom nodes that are explicitly designed to work with your setup.
Efficiency: ComfyUI is known for being very memory efficient. When you're dealing with video models that eat VRAM for breakfast, this is a HUGE advantage.
The Setup: Getting Your System Ready for AI Video
Okay, here's the meat & potatoes. We're going to walk through the general process of setting up a local AI environment on an AMD GPU. The exact steps can vary slightly depending on your operating system (Linux generally has more mature ROCm support, but it's very doable on Windows via WSL - Windows Subsystem for Linux), but the principles are the same.
A Word of Warning: This will involve using the command line. Don't be scared! It's mostly just copying & pasting commands. Take it one step at a time.
Step 1: Install Your Drivers & ROCm
First things first, you need the right foundation. This isn't just about getting the latest graphics driver from the AMD website. You need to install the full ROCm stack.
For Linux Users: This is the most direct path. AMD provides detailed instructions on their site for different distributions like Ubuntu. It typically involves adding AMD's repository to your system & then using your package manager to install the
1
rocm-dkms
package. You'll also want to add your user to the
1
render
&
1
video
groups.
For Windows Users: The recommended path here is using WSL2 (Windows Subsystem for Linux). This lets you run a full Linux environment directly within Windows, giving you access to the better-supported Linux ROCm drivers. AMD even has a blog post on "Running ComfyUI in Windows with ROCm on WSL" that is a great resource. You'll install a Linux distro (like Ubuntu) from the Microsoft Store, then follow the Linux installation steps for ROCm from within your WSL terminal.
Step 2: Python, Git, & Your Virtual Environment
AI runs on Python. You'll need to have Python installed (a version like 3.10 is a safe bet). You'll also need Git, a version control system used to download software from repositories like GitHub.
Crucially, you'll want to use a Python virtual environment. This is a self-contained directory that holds all the specific libraries & packages for a project. This is SUPER important because different AI tools can require different (and sometimes conflicting) versions of libraries. A virtual environment keeps everything neat & tidy.
You can create one with a simple command:
1
python3 -m venv my_ai_env
And then you "activate" it before you start installing stuff:
1
source my_ai_env/bin/activate
Step 3: Installing PyTorch for ROCm
This is one of the most critical steps where people go wrong. The standard version of PyTorch is built for CUDA. You need to install the version built specifically for ROCm. The PyTorch website has a handy tool where you can select your setup (PyTorch Build, OS, Package, Compute Platform) & it will give you the exact command. For AMD, you'll choose ROCm as your compute platform.
Then, navigate into the new directory & install its dependencies:
1
cd ComfyUI
1
pip install -r requirements.txt
Once that's done, you can launch it by running
1
python main.py
. If everything is set up correctly, it will start a local server & you can access the ComfyUI interface in your web browser.
Making Movies: Image-to-Video Models & Techniques
Okay, you've got ComfyUI running. Now for the fun part. The world of image-to-video is exploding, but a few key techniques & models are at the forefront.
1. AnimateDiff
AnimateDiff is probably the most popular method right now for adding motion to still images generated with Stable Diffusion. It’s not true video generation from scratch; it’s more like "motion injection." You give it a starting image (or just a text prompt) & it uses a "motion module" to animate it over a short duration, usually a few seconds.
To use it in ComfyUI, you'll need to:
Install the AnimateDiff Custom Node: You can do this easily using the ComfyUI Manager (a must-have custom node for managing other extensions).
Download Motion Modules: These are the models that actually contain the animation data. There are various versions (v1, v2, v3) that produce different styles of movement. You can find them on sites like Civitai.
Build your Workflow: You’ll add the AnimateDiff nodes to your standard image generation workflow. This allows you to control things like the number of frames & how the motion is applied.
A word of warning: AnimateDiff can be VERY VRAM hungry. A Reddit user noted that a simple 512x512 animation that took ~5.6GB of VRAM on an NVIDIA card took a whopping 16.3GB on their AMD card with ROCm. This highlights that even with ROCm, there can still be optimization differences. So start with short, low-resolution generations to see what your card can handle.
2. ControlNet for Video
ControlNet is a game-changer for image generation, & its principles are just as powerful for video. It lets you guide the AI's output using a control map, like a depth map, a character pose, or line art.
For video-to-video tasks, this is incredible. You can shoot a simple video of yourself, run it through a preprocessor to create a series of OpenPose skeletons or depth maps, & then use that as a guide for ControlNet to generate a completely new, stylized character that mimics your exact movements.
Using this in ComfyUI involves loading a ControlNet model, feeding it your sequence of control maps (one for each frame of your video), & connecting it to your generation workflow. The Amuse software also demonstrates a video-to-video workflow using depth maps, which shows how powerful this technique can be for maintaining consistency.
3. Emerging Models
New models are popping up all the time. Keep an eye out for ones that are getting good results & have been tested on ComfyUI. Models from companies like Alibaba (e.g., Wan2.1) and others are often available as ComfyUI nodes soon after release. The key is to look for models that are released with open weights, making them accessible for local use.
The Business Angle: Beyond Just Making Cool Gifs
This is all fun for personal projects & art, but there's a serious business side to this technology. Think about it: small businesses & startups can now create custom video content, product animations, & marketing materials at a fraction of the cost of traditional video production.
But this also creates a new challenge: customer interaction. When a business starts using AI to generate content & automate processes, customers will inevitably have questions. They'll want to know how a product works, what the new features are, or where to find something on the website. This is where AI-powered communication becomes essential.
This is exactly the kind of problem Arsturn is built to solve. While you're using your local GPU to cook up amazing video content, you also need a way to manage the increased engagement it might bring. Arsturn helps businesses create custom AI chatbots trained on their own data. So, when a visitor lands on your site, impressed by your AI-generated product demo, they can get instant answers to their questions 24/7. It's a no-code platform, meaning you don't need to be a developer to build a chatbot that understands your business inside & out. It helps bridge the gap between creating amazing AI content & providing the top-notch, instant customer support that modern audiences expect.
For businesses looking to automate lead generation & website optimization, building a personalized chatbot is a no-brainer. This is where Arsturn shines as a business solution. It’s a conversational AI platform designed to help businesses build meaningful connections with their audience. By training a chatbot on your company's specific documents, website content, & product information, you can provide personalized experiences that guide visitors, answer complex questions, & ultimately boost conversions, all without writing a single line of code.
Final Thoughts & Encouragement
Look, diving into the world of local AI on an AMD GPU is a journey. It's not always a straight road. You will run into errors. You will have to troubleshoot. You will probably spend an afternoon trying to figure out why a specific Python library isn't working.
But it is SO worth it.
The feeling of getting it all set up, of building your first AnimateDiff workflow in ComfyUI, & seeing your own creation come to life, frame by frame, generated entirely on your own hardware—it's pretty magical. You're at the cutting edge of a creative revolution, & you're doing it with the hardware you already have.
The community is your greatest resource. Reddit communities like r/StableDiffusion & r/AMD are full of people who have run into the same problems you will. YouTube has countless guides for specific setups. Be patient, be persistent, & don't be afraid to ask for help.
Hope this was helpful. The barrier for AMD users is getting lower every day, & the creative possibilities are exploding. Now go make something awesome. Let me know what you think.