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If you have over 4MB VRAM you can run it locally. I've been experimenting recently and find that even with 10MB VRAM I can only get 256x256 resolution images.
by gibbonsrcool 4y ago
If you have over 4MB VRAM you can run it locally. I've been experimenting recently and find that even with 10MB VRAM I can only get 256x256 resolution images. I have a Dockerfile I can share that packages up the install process and removes censorship if anyone is interested. I find the censoring is extremely conservative.
- arthurcolle 4y agoPlease do share. I did the same and have it but having trouble deploying it to a prod GPU server, still figuring it out.
- gibbonsrcool 4y agoHere you go: https://gist.github.com/gvbl/9231406c54e7c9fd37abdfa6c697fe5f https://gist.github.com/gvbl/9231406c54e7c9fd37abdfa6c697fe5... You can run it with a command like this (I'm on windows): docker run -it -v <model file path>:/stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt -v <outputs folder>:/stable-diffusion/outputs -v <inputs folder>:/stable-diffusion/inputs -v <cache folder>:/root/.cache --gpus all knightley python /stable-diffusion/scripts/txt2img.py --W 256 --H 256 --prompt "a horse wearing a top hat" assuming you build the image and tag it "knightley"
- iforgotpassword 4y agoHuh, I've been using the docker container by cmd2 and been doing 512x512 just fine with 8GB. Are you on Windows by any chance?
- gibbonsrcool 4y agoYes, windows with a RTX 2070 Super. The logs say the app is trying allocate just a few hundred MB more than what I have. I'm reasonably happy with 256x256 for now, just messing around.