3 ms·
Use halfprecision float and/or the optimized forks https://github.com/basujindal/stable-diffusion https://github.com/basujindal/stable-diffusion https://githu
by schleck8 4y ago
Use halfprecision float and/or the optimized forks
https://github.com/basujindal/stable-diffusion https://github.com/basujindal/stable-diffusion
https://github.com/neonsecret/stable-diffusion https://github.com/neonsecret/stable-diffusion
Or the hlky webui, that is optimized too.
http://rentry.co/kretard http://rentry.co/kretard
- baobabKoodaa 4y agoI've been trying to get the basujindal fork to work, but it seems to be putting all work on the CPU. I've been running the example txt2img prompt for 30 minutes now and it's still not finished. It has reserved 4Gb memory from the GPU, but the GPU doesn't appear to be doing any work, only CPU is doing work.
- deleted 4y ago[deleted]
- prettydeep 4y agoUse the original SD repo. But modify the txt2img.py according to: https://github.com/CompVis/stable-diffusion/issues/86#issuecomment-1230309710 https://github.com/CompVis/stable-diffusion/issues/86#issuec...
- baobabKoodaa 4y agoI now did everything I could to constrain the memory usage of the original SD repo, I was finally able to get it to run, and it produced green squares as output :( What I did: - scripts/txt2img.py, function - load_model_from_config, line - 63, change from: model.cuda() to model.cuda().half() - removed invisible watermarking - reduced n_samples to 1 - reduced resolution to 256x256 - removed sfw filter Just can't get it to work and it's not producing an error message or anything that I could debug it with.
- rrobukef 4y agoYour model is overflowing/underflowing generating NaNs. I got it with memory optimised, increased resolution (multiples of 32, 384 x 384) and full precision while keeping it in 4 GB.
- deleted 4y ago[deleted]