3 ms·
Nvidia GTX 1660 Super with 6GB of VRAM. I do runs at 384px by 384px, with batch size of 1. Sampling method has almost no impact on memory. Using k_euler with 3
by BatteryMountain 4y ago
Nvidia GTX 1660 Super with 6GB of VRAM.
I do runs at 384px by 384px, with batch size of 1. Sampling method has almost no impact on memory. Using k_euler with 30 steps renders an image in 10 to 20 seconds. The biggest thing that affect rending speed is the steps and the resolution, so 512x512 with C 50 using ddim is much slower than 256x256 with C 25 using k_euler.
The sampling methods run mostly in the same timelines, but the k_euler one can produce viable output at lower C values, meaning it is faster than the rest.
Don't add gfpgan in the same pipeline, as it takes more vram.
I'm running it on Windows 10 with latest drivers. I set the python process to Realtime priority in task manager (makes a slight difference!). Have not tried it on Linux.
- gitfan86 4y agoI'm running 1660 ti on Windows 11. I'm thinking about getting a 3090 so that I can make higher resolution images. Gfpgan runs much faster for me 5 seconds per picture
- machineleaning 4y agoWhat resolution could you get on a 3090?
- gitfan86 4y agoNo idea, things are changing very quickly now
- BatteryMountain 4y agoGood way to know (if not found online) is to start at 512x512 if you have a card with 12GB VRAM and try to increment it (the ui slider increases it by 64px for each increment) and backtrack when you start getting "cuda out of memory" errors. I've seen some renders on discord where the sizes are well above 1000px, so they must have had a 16/24GB cards or something similar. In the research context, they are used to using 40GB/80GB hardware (and perhaps multiples) to train and render. So quite remarkable that it works on consumer hardware at this point. edit: on second thought, they most likely rendered at 512px but then ran it through an upscaler model. I've been meaning to hook mine up but kinda forgot to try.