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2026-09-23 21:51:44 +02:00

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ComfyUI for gfx1151 (Ryzen AI MAX)

Dockerized ComfyUI with PyTorch & flash-attention for gfx1151 (AMD Strix Halo, Ryzen AI Max+ 395), relying on AMD's pre-built and pre-configured environment (no custom wheels).

Versions used:

ROCm: 7.2
PyTorch: 2.9.1
Python: 3.12
ComfyUI (built-in): v0.15.0

Last updated & tested: Feb 25, 2026, on 6.18.9 (ArchLinux), AMD RYZEN AI MAX+ 395 (Framework Desktop), with opencl-amd packages (7.2.0-1).

Caution

I kinda understand what's going on here, but not fully. It took me most of the day to figure out how to run ComfyUI on my Framework Desktop without it crashing (which is absurd for a CPU with AI in the name), and the final working solution turned out to be much simpler than what I was able to find initially.

That being said, it works (as of Feb 25, 2026), but I'm not sure if there's an even better / more correct way of achieving the same thing. Same goes for the environment variables that are supposedly making ComfyUI faster / resource efficient.

I just want to share this solution to save someone else a couple of hours ¯_(ツ)_/¯

Get started now

The Docker image is published to Docker Hub, so you can, but don't have to build it yourself.

There are two options:

Copy docker-compose.yml and run docker compose up -d
Copy docker-run.sh and run ./docker-run.sh. After the first run, use docker start comfyui-gfx1151.

ComfyUI will be available at http://localhost:8188.

The starter templates should generate images without any issues.

Once you've verified that it works, feel free to use this repository as the foundation for your own setup or workflow.

Parameters

Both options have the same pre-configured parameters, which are:

Allocate 8GB of shared memory (shm_size) for internal PyTorch / ComfyUI shenanigans, this should be plenty, feel free to lower it. This should NOT be > than available RAM. This is NOT allocating VRAM.
Mount ./ComfyUI for the root of ComfyUI. If the directory is empty when the container starts, it will copy a pre-cloned (baked in) version of ComfyUI. If it's not empty, it will be used to run ComfyUI located in it. You can update this directory manually to use newer version of ComfyUI without having to re-download the image
Expose port 8188 for ComfyUI
Add video + rendering devices and groups. While this just works on Arch, it might require some pre-requisite steps on Ubuntu, I haven't checked.

There are a couple of scripts that can check that both PyTorch and flash-attention work, you can find them below.

Updating ComfyUI / dependencies

If you need to install custom nodes or refresh ComfyUI dependencies after a manual update, you can do it from within the container (until #4 is resolved):

docker exec -it comfyui-gfx1151 /bin/bash

cd /opt/ComfyUI

pip install -r requirements.txt

What's inside / how to replicate

This image is based on AMD's rocm/pytorch image that has Ubuntu 24.04, ROCm 7.2, Python 3.12 and PyTorch 2.9.1, in which everything is configured to work together and it just works. You can find out more about this image in AMD's ROCm documentation.

There are only two missing pieces which this image adds: flash-attention and, well, ComfyUI.

There's nothing specific about the ComfyUI installation, you can actually bring your own, it should work.

flash-attention, however, "doesn't work" out of the box if you run AMD's image. I'm saying "doesn't work" because, as far as I understand, it doesn't have the frontend for it (the APIs), but it does have the backend: Triton. So flash-attention can be "installed" with a special env variable FLASH_ATTENTION_TRITON_AMD_ENABLE, which makes ComfyUI and other tools using flash-attention think that flash-attention is installed and works (even though it's triton under the hood, which is actually doing the job). You can see the lines that install it in Dockerfile, and if you try to do it yourself, you'll notice that it executes very fast (because flash-attention isn't actually built in full).

It's worth noting that flash-attention is cloned from a specific branch main_perf -- I'm not sure why exactly, I haven't checked, but I assume it's because it has (stable?) support for Triton which is not yet in the main branch, see this issue. I basically copy-pasted this part from other installations (vLLM and repos by kyuz0), so I hope they know what they're doing :D

In scripts there are two scripts that can check if PyTorch and flash-attention work as expected and utilize the iGPU. I used these when looking for a solution, they proved to be helpful, so I'm adding them to the image in case something breaks or doesn't work as expected, maybe they'll help debug the problem or something.

With that knowledge, you should be able to take Dockerfile and build an image yourself.

If any of this makes more sense to you than it does to me and you know how to improve something or can add a helpful comment with additional context, please do!

What I tried that didn't work

The majority of other solutions seem to rely on custom-built wheels, such as the image by pccr10001/comfyui-gfx1151-fa.

I never managed to make these custom wheels work, presumably because of non-locked dependencies (pulling in newer version of rocm/etc with old wheels). However, they made me begin to understand what was happening and how to move forward, so huge thanks to everyone who left any comments on the topic.

Some other solutions also relied on the image ghcr.io/rocm/therock_pytorch_dev_ubuntu_24_04_gfx1151, which is no longer published, so I never got that working either. The image I'm referencing (rocm/pytorch) seems like a replacement for it though?

Initially, I copied over some environment variables that were supposed to speed up ComfyUI / PyTorch and make it more resource efficient: PYTORCH_TUNABLEOP_ENABLED, MIOPEN_FIND_MODE and ROCBLAS_USE_HIPBLASLT (not adding them as a codeblock to avoid someone copy-pasting them). However, at least one of them not only made it worse when it comes to the speed, but I believe it would crash my display server (X11) every now and then when running stable diffusion models. Apparently, this is relatively common to see with AMD drivers in general, so I'm not entirely sure that those env variables were 100% responsible for the crashes (might've been something else), but removing all of them helped (at least for now), so I've removed them from this repo's scripts too. If you also experience display server crashes, let me know.

Tests

There are two scripts that you can use to test if everything works correctly

Test PyTorch

While the container is running, running

docker exec -it comfyui-gfx1151 /bin/bash /opt/comfyui-gfx1151-utils/test-pytorch.sh

should produce NO errors. The output should be something like:

GPU: AMD Radeon Graphics | FlashAttn: True
Mean: -0.026233481243252754

Test flash-attention

While the container is working, running

docker exec -it comfyui-gfx1151 python3 /opt/comfyui-gfx1151-utils/test-pytorch-flashattention.py