Lightrun Answers was designed to reduce the constant googling that comes with debugging 3rd party libraries. It collects links to all the places you might be looking at while hunting down a tough bug.
And, if you’re still stuck at the end, we’re happy to hop on a call to see how we can help out.
thanks (it's 10x faster than JAX)!
See original GitHub issueI’ve been trying to get dalle-playground running performantly on M1, but there’s a lot of work remaining to make the JAX model work via IREE/Vulkan.
so, I tried out your pytorch model,
with a recent nightly of pytorch:
pip install --pre "torch>1.13.0.dev20220610" "torchvision>0.14.0.dev20220609" --extra-index-url https://download.pytorch.org/whl/nightly/cpu
…and it’s 10x faster at dalle-mega than dalle-playground was on JAX/XLA!
using dalle-mega full:
wandb artifact get --root=./pretrained/dalle_bart_mega dalle-mini/dalle-mini/mega-1:latest
generating 1 image took 27 mins on dalle-playground (using 117% CPU), whereas this pytorch model runs in 2.7 mins (using 145% CPU)!
GPU looks less-than-half utilized. haven’t checked whether pytorch is the process that’s using the GPU.
these measurements are from M1 Max.
bonus
“crystal maiden and lina enjoying a pint together at a tavern”
Issue Analytics
- State:
- Created a year ago
- Reactions:10
- Comments:14 (2 by maintainers)
Top Results From Across the Web
Top Related Medium Post
Top Related StackOverflow Question
Troubleshoot Live Code
Top Related Reddit Thread
Top Related Hackernoon Post
Top Related Tweet
Top Related Dev.to Post
Top Related Hashnode Post
Even faster these days: you get a 4x4 grid instead of a 3x3 grid on Replicate, after the same duration.
However, this is based on Dall-E MEGA instead of Dall-E Mini, so results might differ. Not sure if better or worse.
I also tried using
.contiguous()on any tensor that would be transferred to the MPS device:https://github.com/Birch-san/min-dalle/commit/b1cf6c284a949d23f8c0cd6802bb207b876bf2af
still black.