3 ms·
I meant images generated by the model. Now that I think of it I could just send you the sampled vectors and you could feed that to the vector to image part.
by juliendorra 4y ago
I meant images generated by the model. Now that I think of it I could just send you the sampled vectors and you could feed that to the vector to image part.
- jimmySixDOF 4y agoMy understanding is that images will not be bit identical due to GPU physics and decimal precision. Images from the same seed may be for all practical intents and purposes indistinguishable - but there are some flipped bits involved.
- fragmede 4y agoThat's not my understanding. The same seed value to the device's random number generator should results in the exact same outputs - there's a bug being chased down in the MPS (MacOS) backend where the fixed random seed doesn't output the same image on different computers.
- squeaky-clean 4y agoI've heard something a bit in between what you're both saying. For the same machine with the same seed / parameters [0], your output is deterministic. But once you change hardware or OS you will probably get bit-level differences that won't make a macro-level difference. No idea how true that is, but on my windows machine, same params/seed is definitely deterministic. [0] a help string in the SD source code recommends the ddim_eta parameter (which isn't exposed in most web UI or GUI's, including the OP github) stay at the default 0.8 for deterministic sampling. I have no idea if this means changing the value from 0.8 produces non-deterministic results with the same hardware/os/params/seed. Or if they just mean changing this from 0.8 will make your SD not match the online model but still be deterministic itself. But in my testing, changing this value gives no useful changes to the image generation, so I keep it at 0.8
- holoduke 4y agoIf floats are used than there is no absolute deterministic behaviour accross different machines. It can never be guaranteed.