stable-diffusion

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Sep 4, 06:00	2022-09-04 06:00:00 UTC	2022-09-04 07:00:00 UTC	1	1
Sep 4, 07:00	2022-09-04 07:00:00 UTC	2022-09-04 08:00:00 UTC	0	0
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Sep 4, 11:00	2022-09-04 11:00:00 UTC	2022-09-04 12:00:00 UTC	2	2

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schleck8·Sep 4, 2022

Use halfprecision float and/or the optimized forks https://github.com/basujindal/stable-diffusion https://github.com/neonsecret/stable-diffusion Or the hlky webui, that is optimized too. http://rentry.co/kretard

In Running Stable Diffusion on Your GPU with Less Than 10Gb of VRAM

schleck8·Sep 4, 2022

Also there are two optimized forks which run on < 6 GB https://github.com/basujindal/stable-diffusion https://github.com/neonsecret/stable-diffusion

In Running Stable Diffusion on Your GPU with Less Than 10Gb of VRAM

TaylorAlexander·Sep 4, 2022

In case anyone is confused by the clashing repos, here is how I was able to easily run this updated code. Clone the original SD repo, which is what this code was built off of, and follow all the installation instructions: https://github.com/CompVis/stable-diffusion In that repo, replace the file ldm/modules/attention.py with this file: https://raw.githubusercontent.com/neonsecret/stable-diffusio... Now run a new prompt with a larger image. Note that the original model was trained on 512x512 and may lead to repetition especially if you try to increase both dimensions (this is mentioned in the SD readme) so just run with one dimension increased. For example try the following example: python scripts/txt2img.py --prompt "a person gardening, by claude monet" --ddim_steps 50 --seed 12000 --scale 9 --n_iter=1 --n_samples=1 --H=512 --W=1024 --ski

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p_grid I confirmed that if I run that command with the original attention.py, it fails due to lack of memory. With the new attention.py, it succeeds. That said, this still uses 13GB of ram on my system. I suppose you can check out the full repo with the updated code, which seems to have other changes, if you want to give that a try. https://github.com/neonsecret/stable-diffusion/ I have already been using the original SD repo so I found benefit by just changing attention.py

In Stable Diffusion PR optimizes VRAM, generate 576x1280 images with 6 GB VRAM