Disco Diffusion Tips

README.md

For something like https://www.instagram.com/holosomnia/

• 768 x 1024

• High guidance scale 18k and above,

• 200-250 steps,

• 4000+ tv scale,

• lower range scale to 80,

• higher sat scale 2000+,

• no secondary model (important)

• Add ViT14 or if have ram, 336 (very nice)

• bump eta to 0.9 (important)

• cut ic pow to 10

• split the cut schedule into 200s and do like 10/8/6/2/0 for overview and the opposite for the innercut, and just play with the numbers a bunch

• prompts: always add beeple for blur, orbs and color. to remove orbs, do "globe:-1". To reduce blur, do "dof:-1"

• kinkade for color.

• Try various "color-heavy" artists.

• some artists dont' do much, and some really change the result

• Use prompts that make sense for the artists

• Add prompts and weights to remove aspects as you iterate

• Just try a LOT of variations

• ALWAYS use partial saves and take the 90%-ish partial save as your final and ALWAYS run it through Real-ESRGAN Inference Demo.ipynb to upscale and make it crisp

Example prompt:

[
  "A beautiful ultradetailed anime illustration of a city street by beeple, makoto shinkai, and thomas kinkade, anime art wallpaper 4k, trending on artstation:3",
  "anime",
  "car:-1",
  "dof:-1", 
  "blur:-1"
]

Hey friend~ I dove into the world of Ai art less than 24 hours ago... this post has been insanely helpful. Many thanks!!

P.S. Any chance you have a link for a tutorial or breakdown on how to run images through the Real-ESRGAN for upscaling?

I get RuntimeError: CUDA out of memory. Tried to allocate 1.41 GiB (GPU 0; 15.90 GiB total capacity; 12.12 GiB already allocated; 993.75 MiB free; 14.02 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
each time I uncheck 'use secondary model`
How do you avoid that?
I'm pro+...

good ,perfet

I get RuntimeError: CUDA out of memory. Tried to allocate 1.41 GiB (GPU 0; 15.90 GiB total capacity; 12.12 GiB already allocated; 993.75 MiB free; 14.02 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF each time I uncheck 'use secondary model` How do you avoid that? I'm pro+...

hey there - not sure if you were able to figure it out on your end, but for anyone else -- under resources settings, there is an option to 'change runtime type' to premium. helped me avoid the CUDA issue

what do you mean by 'cut ic pow to 10' exactly? when I try to alter this I get broken code when executing