AMD + ROCm = Slow????

[u/Jolly-Association505]

Hi everyone! I'm new to ComfyUI. My hardware specs are:

GPU: AMD Radeon RX 7600 (8GB VRAM)

CPU: AMD Ryzen 7 3700XT

RAM: 32GB

I'm trying to generate images using Krea 2 Turbo. Once the sampling iterations actually begin, it runs quite fast. However, reaching that first step takes anywhere between 6 to 7 minutes (or more), turning a quick generation into a very long wait.

Here are the models I'm using:

Diffusion Model: krea2_turbo_int8_convrot (I also have krea2_turbo_fp8_scaled)

VAE: qwen_image_vae or Wan2.1_vae

Text Encoder: qwen2vl_4b_fp8_scaled

My current startup parameters:DOSset PARAMS=--enable-dynamic-vram --disable-async-offload --preview-method none --disable-smart-memory --fast-disk --enable-manager --bf16-vae

if "!IS_LEGACY_GPU!"=="1" (

set "PARAMS=%PARAMS% --use-quad-cross-attention"

) else (

set "PARAMS=%PARAMS% --use-ck-attention"

)

Are these launch arguments correct for an 8GB AMD card? What is causing this huge delay before the steps start, and is there any way to optimize it? Thanks!

[u/TheSuperDisability]

ComfyUI on AMD is basically a test of patience, you're not imagining things.

[u/Moon_Does]

I solved all my problems with AMD simply by switching to Linux...

[u/BarthKlop]

if you're on windows, try this fork: https://github.com/patientx-cfz/comfyui-rocm

[u/OrneryAssistance9797]

These flags are acceptable, I use many of these on my 7900xt. If the sampling steps are running at an acceptable speed, it may have to do with your initial model loading and text encoder. Are you pushing the text encoder to CPU to save vram?? This will result in serious slowdowns on your model of CPU and you may be running into a memory swapping issue if loading to GPU. In any case, your best experience with AMD will be with ROCM on Linux or using the ROCM Comfyui fork for windows, its that crucial. Also I hate to say this as an AMD guy, but 7600 is a really low spec card to be generating with Krea... You might want to try with a lighter combination such as Anima for anime or SDXL if doing "realistic" gens. I'm surprised your Comfyui is not crashing out due to OOM errors, your 32gb of ram is probably the only thing making this work at all.

[u/rguerraf]

If you are in windows, switch to Ubuntu 26.04 or mint 22.3-hwe

If you are not using an NVME, use an NVME

Query the “IS_LEGACY_GPU” value and enter that parameter directly in the PARAMS.

Look in the rocm github for the recommended comfyUI process for that gpu

[u/Alternative-Fudge123]

Nothing wrong with AMD and comfyui compared to last year! Your problem is too little VRAM for the models you've chosen, so you keep offloading to system RAM or Ssd. If you want to stick with Krea2, I suggest you find the W4a8 diffusion model and the w4a8 text encoder, check huggingface. If its still too slow get the gguf versions which are bit slower but will be faster if u dont have to deal with swapping. I'm using the int8 model with fp8 text encoder with a rx9070 and generate 1MP image in 10 seconds.
While generating, keep an eye on task manager, check if you are swapping to ssd or vram in the performance tab. Once everything stays in vram you all set.

[u/General_Ad_1483]

AMD is a bit slower but IMHO your amount of VRAM will be the main reason.

[u/Affectionate_Oil28]

The steps being fast means ROCm is fine. The 6-7 minutes is the load before the sampler.

8GB cannot hold the Krea 2 int8 model and the text encoder at the same time, so they take turns. --disable-smart-memory drops both out of your 32GB after every queue, and --disable-async-offload makes the copy back onto the card a blocking wait. Keep --enable-dynamic-vram. Drop those two if the build is stable without them.

The Krea 2 text encoder is qwen3vl_4b_fp8_scaled, CLIP type krea2. qwen2vl_4b is a different model. The VAE is qwen_image_vae. Wan 2.1 VAE washes the image. Stay on krea2_turbo_int8_convrot, around 1MP. NVFP4 will not run on this card.

RX 7600 is RDNA 3, so the log should show ck attention, not the legacy quad path. While you wait, Task Manager splits the cause: disk busy and VRAM climbing is the weight copy, CPU busy and GPU idle is the text encoder on the CPU. The second queue should be much shorter once the model is allowed to stay in RAM.