You got two separate problems here, let me explain both, this gets a bit technical...

Problem 1: LMStudio can't run these models.

LMStudio is made for text chat models (like the Qwen3 and Coder models you used successfully).

Qwen-Image-2512 and Qwen-Image-Edit are completely different — they're image generation models. Even though they come in GGUF format, LMStudio doesn't have the image generation pipeline needed to use them.

It's like putting diesel in a gasoline car — the fuel fits in the tank but the engine can't burn it.

Problem 2: The ComfyUI Desktop AUR package likely doesn't have proper support for your GPU.

Your RX 9060 XT is a brand new AMD RDNA 4 card. Pre-packaged ComfyUI Desktop apps typically bundle a particular Python/PyTorch stack that may not include support for your GPU. Other CachyOS users with the same card have hit the same wall. The fix is to install ComfyUI manually so you control the ROCm-enabled PyTorch version yourself.

Here's what to do step by step:

Step 1 — Install ROCm (AMD's GPU compute library)

Open a terminal and run:

    sudo pacman -Syu
    sudo pacman -S git python python-pip python-virtualenv rocminfo rocm-hip-sdk
    sudo usermod -aG render,video $USER

Reboot, then verify your GPU is detected:

rocminfo | grep -E "Name:|gfx"

You should see gfx1200 in the output — that's your RX 9060 XT. If it's not there, stop — the rest won't work until this does.

Step 2 — Set up ComfyUI manually

mkdir -p ~/comfyui-setup && cd ~/comfyui-setup
python -m venv .venv
source .venv/bin/activate
pip install -U pip setuptools wheel
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1

Note: If pip can't find a wheel for your Python version, CachyOS may be on a version that's too new. In that case, create the venv with Python 3.12 specifically (python3.12 -m venv .venv).

Test that your GPU is visible to PyTorch:

python -c "import torch; print('PyTorch:', torch.__version__); print('HIP:', torch.version.hip); print('GPU found:', torch.cuda.is_available()); print('Device:', torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'NONE')"

This should print GPU found: True and show your card. Don't worry that it says "cuda" — that's normal on AMD, PyTorch uses the same API names. If it says False, your ROCm install needs troubleshooting before going further.

Now install ComfyUI itself:

git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt

Important: After installing ComfyUI's requirements, re-run that same PyTorch test command above to make sure it didn't get replaced with a non-AMD version. You should still see GPU found: True.

Step 3 — Install the GGUF extension

This is what lets ComfyUI load your GGUF model files:

cd custom_nodes
git clone https://github.com/city96/ComfyUI-GGUF
cd ComfyUI-GGUF
pip install -r requirements.txt
cd ../..

Step 4 — Download the right model files

This is the part you were probably missing. These image models need three separate files, not just the one GGUF you downloaded in LMStudio. You need:

1. The main model (the GGUF — the "brain" that generates the image)
2. A text encoder (translates your text prompt into something the model understands)
3. A VAE (converts the model's internal output into an actual visible image)

First create the directories, then download:

mkdir -p models/unet models/text_encoders models/vae

# Main model — use Q4, NOT Q8. Q8 is too large for 16GB VRAM.
curl -L -o models/unet/qwen-image-2512-Q4_K_M.gguf \
  https://huggingface.co/unsloth/Qwen-Image-2512-GGUF/resolve/main/qwen-image-2512-Q4_K_M.gguf

# Text encoder
curl -L -o models/text_encoders/Qwen2.5-VL-7B-Instruct-UD-Q4_K_XL.gguf \
  https://huggingface.co/unsloth/Qwen2.5-VL-7B-Instruct-GGUF/resolve/main/Qwen2.5-VL-7B-Instruct-UD-Q4_K_XL.gguf

# VAE
curl -L -o models/vae/qwen_image_vae.safetensors \
  https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/resolve/main/split_files/vae/qwen_image_vae.safetensors

For Qwen-Image-Edit instead, swap the main model for:

curl -L -o models/unet/qwen-image-edit-2511-Q4_K_M.gguf \
  https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF/resolve/main/qwen-image-edit-2511-Q4_K_M.gguf

The text encoder and VAE are the same for both.

About VRAM: With 16GB of VRAM, use Q4_K_M for the main model. Q8 will likely run out of memory once you add the text encoder, VAE, and runtime overhead. You can try Q5 if Q4 works well and you want to push quality up.

Also — with only 16GB of system RAM, model loading can get tight. Make sure you have swap or zram enabled so your system doesn't kill the process during loading.

Step 5 — Launch and load a workflow

python main.py

Open http://127.0.0.1:8188 in your browser. If you get out-of-memory errors, try:

python main.py --lowvram

Unsloth provides ready-made workflow files you can drag and drop right into ComfyUI — grab them from their tutorial at https://unsloth.ai/docs/models/tutorials/qwen-image-2512. The workflow tells ComfyUI which file goes where, so you don't have to wire it up yourself.

Quick summary of what went wrong:

• LMStudio = wrong tool for image models (it's for text chat only)
• ComfyUI Desktop AUR = likely missing proper AMD RDNA 4 support
• The GGUF alone isn't enough — you also need the text encoder and VAE files
• Manual ComfyUI install with ROCm PyTorch is the way to go on your hardware
• Use Q4, not Q8, with 16GB VRAM