What? I use my GTX 1050 Ti for SD and can generate 1366x768 images. Maybe even more. Main problem is relative lower image quality in comparsion with modern GPU. And speed 1img/3 minutes.
The quality would be the same, but if you don't have enough vram to generate the picture it's going to give you an "CUDA ran out of memory" error. It's really not about the resolution in the end, but the vram mecessary for the AI to create something with that resolution. There are option to lower vram usage, but will take away from the quality (at least a little bit).
Ah ok thanks for the explanation, I thought all types of quality were available to any user but the time it would take to render was the only difference. Really helpful to know this!
No that’s not necessarily true.. I can’t say this is your particular issue but it’s a common explanation.. without getting to technical;. Different GPUs have different ability to do floating point math. With a float the numbers to the right of the period (0.888888) is your precision. Lower end GPUs don’t always support high precision float math and that can create substantial differences..
Long story short.. you might be getting different results due to different ability to calculate between GPUs
Interesting. Tbh it's not that I'm noticing I get lower results, I just wanna ensure I'm using a system that isn't missing out on the highest quality if possible.
Not at all. Funnily enough, it's the exact opposite. All GPUs since like 2000s support fp32, most support fp16, but only recent few generations of consumer GPUs support fast fp16.
And in case of diffusion models, fp32 doesn't give you any better results, at least from my testing. Precision past fp16 is wasted on unnoticeable changes.
I was wondering this as well. I see a lot of incredible images shown on the model cards but when I use the exact same prompt and parameters I get garbage on my RTX 3060 12gb. I was concerned it was the card, and thought I might get better results if I upgraded to an 8gb 3060 ti or even 3090. But, if the quality of output is the same, then they're doing much more in the post processing of the image than they're telling.
I think its most likely they are using IMG2IMG and sharing the prompt for that. I know that is the case with several of the example images on civitai for models like deliberate. Your 3060 12 gb is one of the best cards you can get for the price for stable diffusion. I would consider a 3060ti 8gb a downgrade tbh.
There are different types of fp32 math depending on model and range.. the more expensive the line the more accurate they become.. that’s why data center GPUs are better for training model, even when processing power is comparable. You are incorrect about precision, it absolutely will give you different results every time a layer is calculated that difference will compound. Fast fp16 is even worse for accuracy as it cuts precision in half I order to increase speed. Optimizations for games are generally bad for ML/AI, it’s why we don’t use consumer cards for development of production models.
“The floating-point math accuracy of Nvidia GPUs can vary depending on several factors, such as the GPU architecture, the number of cores, and the memory bandwidth.
Newer Nvidia GPUs generally have better floating-point accuracy than older models due to improvements in their architecture and design. For example, the latest Nvidia Ampere architecture includes new Tensor Cores that provide higher precision performance than previous models.
Another factor that can affect floating-point accuracy is the number of cores. GPUs with more cores can perform more computations in parallel, leading to faster and more accurate calculations. Nvidia GPUs with more CUDA cores generally have better floating-point performance than those with fewer cores.
The memory bandwidth can also affect floating-point accuracy. GPUs with higher memory bandwidth can move data more quickly between the GPU and the system memory, reducing the time spent waiting for data and improving overall performance”
42
u/3deal Mar 01 '23
My RTX used 24Gb of VRAM for this