Captain Suave
Caesar si viveret, ad remum dareris.
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Most of you have seen the text-to-image products of Dall-E, Midjourney, etc. Stable Diffusion is a comparable approach which you can run on your own hardware.
I have an 8GB 2070 Super and kept running out of VRAM with the main version, so I ended up running an optimized fork which exchanges speed for RAM allocation. It still produces images in well under a minute.
There's also a fork which enables HD upscaling without requiring multiple GPUs to hold the problem.
It took a bit of fiddling for me to get everything working. The process requires some comfort with Git repositories and Python, and the model is run via CLI.
There's definitely some art in sculpting the prompts to get what you want. I'm not good at it yet, but there are lots of examples online.
Stable Diffusion Public Release — Stability AI
We are delighted to announce the public release of Stable Diffusion and the launch of DreamStudio Lite.
stability.ai
How to Run Stable Diffusion on Your PC to Generate AI Images
You can generate AI art on your very own PC, right now. Here's how to use Stable Diffusion.
www.howtogeek.com
GitHub - CompVis/stable-diffusion: A latent text-to-image diffusion model
A latent text-to-image diffusion model. Contribute to CompVis/stable-diffusion development by creating an account on GitHub.
github.com
I have an 8GB 2070 Super and kept running out of VRAM with the main version, so I ended up running an optimized fork which exchanges speed for RAM allocation. It still produces images in well under a minute.
GitHub - basujindal/stable-diffusion: Optimized Stable Diffusion modified to run on lower GPU VRAM
Optimized Stable Diffusion modified to run on lower GPU VRAM - basujindal/stable-diffusion
github.com
There's also a fork which enables HD upscaling without requiring multiple GPUs to hold the problem.
GitHub - jquesnelle/txt2imghd: A port of GOBIG for Stable Diffusion
A port of GOBIG for Stable Diffusion. Contribute to jquesnelle/txt2imghd development by creating an account on GitHub.
github.com
It took a bit of fiddling for me to get everything working. The process requires some comfort with Git repositories and Python, and the model is run via CLI.
There's definitely some art in sculpting the prompts to get what you want. I'm not good at it yet, but there are lots of examples online.
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