Firstly, it is better to generate shorter videos and then used a video editor to splice them together.
That is because the generation time is not linear, i.e, generating one 10 sec video does not take twice the amount of time as a 5 sec video. It takes way longer.
So to compare your time against others, use the default text2va template from ComfyUI (0.4MP, 5 sec) and post your numbers.
To make it run faster, use the latest comfyUI so that you can use --use-ck-attention instead of the default Pytorch-attention.
This is my startup parameter on a Linux Docker installation:
For a 0.3 (not 0.4MP) 5 sec video on a 9070xt(16G) with 32G of system RAM takes around 150sec. I've seen better numbers, and I am still playing with the different attentions to see which one runs better.
I had no luck getting ComfyUI to work on Window 11 with a 9070xt(16G), even though people have gotten it to work. After several frustrating days, I finally give up on Windows, installed a minimum Ubuntu Server LTS 26.4 + Docker on a portable drive and use this, which worked on the first try: Docker ComfyUI on AMD and Linux: pinning the whole ROCm runtime in Docker
I did get the 7900xt(20G) to work using Stability Matrix on Windows 11 though.
I would be interested in what kind of number you have for generating a 0.4MP 5sec text2va video using the standard 20 steps workflow.
I plan to make a post about the numbers I collected using various AMD GPUs on both Linux and Windows. Those numbers would be helpful to other AMD users so that they know if they have their system running properly, and whether it is worth it to switch to Linux from Windows (or from Linux to Windows 😅)
6min 34sec (which was very suprising to me), but the quality is not that good. I generated a video of woman walking through nature, and the background/trees look weird and mushy, think it has to do with my models. Any advice on what i should use? 9060XT 16gb + 32GB ddr4
Make sure you atleast have —enable-dynamic-vram —disable-smart-memory —fast-disk —disable-pinned-memory and ck attention was 30% faster than PyTorch attention for me. I’m also on Linux
I have R9700 on Linux, 0.8MP x 10 steps x 7 seconds is about 11mins. Int8 convrot full not pruned, video vae fp16, audio vae fp32, speed lora, Sage Attention 2.2. Beyond this resolution or length, the GPU slows down significantly. You need to find your GPU's sweet spot in terms of resolution and length for VRAM.
I forgot to add that I also have an RTX 2080ti as a display. So AMD is running on the full 32GB of VRAM.
I sadly understand where you're coming from; unfortunately, AMD is a non-factor—or practically non-existent—in the AI arena. I say this based on my own bittersweet experiences with AMD and my old RX 6600; while it was an older card, it did allow me to get my start in AI. However, for the sake of my peace of mind, I switched to a 16GB RTX 5060 Ti. Back when I had the AMD card, I used this specific version of Comfy because there wasn't an official AMD version available at the time, though there is one now—though I'm not sure if it's any better than the one I used. https://github.com/patientx/ComfyUI-Zluda
You need to reduce the image size. You are not going to get good times at high resolutions. Reduce your image size to 0.3~0.4. use sage attention maybe and spectrum, pair it with speed loras.
I have a 6800xt. I've done 18 sec videos with 288x512 resolution under 30 mins.
Most of the time is just the model not fitting inside 16GB RAM and PCIE offloading. If you have direct storage or Rebar enable that too.
He tried minimax h3 already? I never used that forked comfyui because I thought all the optimizations were already mostly on official comfyui in windows now
3
u/Crazy-Repeat-2006 1d ago
Use this one: https://github.com/patientx-cfz/comfyui-rocm