31–40 of 184 posts
Atila from Apple on the expected performance: > For distilled StableDiffusion 2 which requires 1 to 4 iterations instead of 50, the same M2 device should generate an image in https://twitter.com/atiorh/status/1598399408160342039
SD2 is the one that was neutered, right? Maybe a dumb question but can the old model still be run?
Earlier quoted context omitted.
Oh gosh that's an intimidating installation process. I'll be much more interested when I can just `brew install` a binary.
A bit different take is DiffusionBee, if you're curious to try it out in a GUI form. https://diffusionbee.com
This may sound naive, but what are some use cases of running SD models locally? If the free/cheap options exist (like running SD on powerful servers), then what's the advantage of this new method?
If you mean monetary usecases: Roughly something like Photoshop/Blender/UnrealEngine with ML plugins that are low latency, private, and $0 server hosting costs.
This may sound naive, but what are some use cases of running SD models locally? If the free/cheap options exist (like running SD on powerful servers), then what's the advantage of this new method?
I'm not sure exactly what that costs me in terms of power, but it is assuredly less than any of these services charge for a single image generation.
https://github.com/apple/ml-stable-diffusion
Oh gosh that's an intimidating installation process. I'll be much more interested when I can just `brew install` a binary.
I'm not seeing any installation instructions on either link - what am I missing?
Atila from Apple on the expected performance: > For distilled StableDiffusion 2 which requires 1 to 4 iterations instead of 50, the same M2 device should generate an image in https://twitter.com/atiorh/status/1598399408160342039
Last nail in the coffin for DALL·E.
Dall-e et. al will still be able to bandwagon off of all the free ecosystem being built around the $10M SD1.4 model that is showing what is possible.
E.g. Dall-e could go straight to Hollywood if their model training works better than SD’s. The toolsets will work
This may sound naive, but what are some use cases of running SD models locally? If the free/cheap options exist (like running SD on powerful servers), then what's the advantage of this new method?
Atila from Apple on the expected performance: > For distilled StableDiffusion 2 which requires 1 to 4 iterations instead of 50, the same M2 device should generate an image in https://twitter.com/atiorh/status/1598399408160342039
With the full 50 iterations it appears to be about 30s on M1. They have some benchmarks on the github repo: https://github.com/apple/ml-stable-diffusion For reference, previously I was getting about <3 minutes for 50 iterations on my Macbook Air M1. I haven't yet tried Apple's implementation but it looks like a huge improvement. It might take it from "possible" to "usable".