3 ms·

They also announced a faster version of Stable Diffusion, supposedly: https://deci.ai/blog/decidiffusion-1-0-3x-faster-than-stable-diffusion-same-quality/ https

They also announced a faster version of Stable Diffusion, supposedly: https://deci.ai/blog/decidiffusion-1-0-3x-faster-than-stable-diffusion-same-quality/ https://deci.ai/blog/decidiffusion-1-0-3x-faster-than-stable... I haven’t personally tested either yet.

brucethemoose2 3y ago
Uhhh, 50 iterations isnt really standard in diffusion land anymore. Neither is unmodified HF diffusers (or worse, the SAI implementation). Some of the techniques they describe are neat though
- SubiculumCode 3y ago
  how many iterations are usual these days? In terms of unmodified diffusers, you mean fine tuned to whatever they are working on? I agree.
  - brucethemoose2 3y ago
    > how many iterations are usual these days? I dunno about a precise number...Less than 30? The UniPC scheduler in particular can get great results below 20 iterations. Karras DPM schedulers are pretty good with 30. > In terms of unmodified diffusers, you mean fine tuned to whatever they are working on? No, people hack non-default augmentations onto the vanilla diffusers (or SAI) pipe to increase speed or increase quality. Some examples included the channels last memory format, downsampling some stages to FP16, TomeSD, self attention, and some more user-guided augmentations like controlnet or tiled diffusion. "alternate" runtimes like the ones available via torch.compile or AITemplate provide some pretty massive speed gains (~2x) if you need them, but are less common since they are hard to install and HF diffusers only (where most of the SD community uses the original SAI implementation). Just as an example implementation, VoltaML is quite speedy out-of-the-box, with most of the sane optimizations already implemented, though its missing some features (like tiled diffusion/area prompting) that the SAI based pipelines have: https://github.com/VoltaML/voltaML-fast-stable-diffusion https://github.com/VoltaML/voltaML-fast-stable-diffusion

brucethemoose2 3y ago

Uhhh, 50 iterations isnt really standard in diffusion land anymore. Neither is unmodified HF diffusers (or worse, the SAI implementation). Some of the techniques they describe are neat though

SubiculumCode 3y ago
how many iterations are usual these days? In terms of unmodified diffusers, you mean fine tuned to whatever they are working on? I agree.
- brucethemoose2 3y ago
  > how many iterations are usual these days? I dunno about a precise number...Less than 30? The UniPC scheduler in particular can get great results below 20 iterations. Karras DPM schedulers are pretty good with 30. > In terms of unmodified diffusers, you mean fine tuned to whatever they are working on? No, people hack non-default augmentations onto the vanilla diffusers (or SAI) pipe to increase speed or increase quality. Some examples included the channels last memory format, downsampling some stages to FP16, TomeSD, self attention, and some more user-guided augmentations like controlnet or tiled diffusion. "alternate" runtimes like the ones available via torch.compile or AITemplate provide some pretty massive speed gains (~2x) if you need them, but are less common since they are hard to install and HF diffusers only (where most of the SD community uses the original SAI implementation). Just as an example implementation, VoltaML is quite speedy out-of-the-box, with most of the sane optimizations already implemented, though its missing some features (like tiled diffusion/area prompting) that the SAI based pipelines have: https://github.com/VoltaML/voltaML-fast-stable-diffusion https://github.com/VoltaML/voltaML-fast-stable-diffusion