I wasn't able to pull some images and I lost 1h trying to diagnose network problems in my setup, but it didn't occur to me that "la liga" was the root cause . My workaround was to add "registry-mirrors": ["https://mirror.gcr.io"] in my /etc/docker/daemon.json
Related to this, I highly recommend anyone to install github.com/ActivityWatch/activitywatch, it's an amazing tool to keep track of your computer use completely locally. I think there are lots of possibilities with data analysis/AI aimed to improved one self's life
This Christmas I bought my mom a new computer because her old one (W10) was falling apart. It took some time but I managed to convince her to give ubuntu a try instead of moving to W11. After a week of complaints and stubbornness, she got really surprised about the lack of annoying prompts asking for updates, dark patterns to switch to edge, promotions etc. Now she has fully adapted and for her basic needs (browsing, reading pdfs, editing spreadsheets) she's basically set for life
It's shocking to read the headlines about the latest direction windows is taking and how user unfriendly is becoming
To me it's very clear that the icons have that "stable diffusion trying to make pixel art" style. I think this needs an extra layer of code that gets the generated image and turns it into actual pixel art
It stays around 26Gb at 512x512. I still haven't profiled the execution or looked much into the details of the architecture but I would assume it trades off memory for speed by creating caches for each inference step
Incredibly fast, on my 5090 with CUDA 13 (& the latest diffusers, xformers, transformers, etc...), 9 samplig steps and the "Tongyi-MAI/Z-Image-Turbo" model I get:
Super interesting project, at first I thought it would be a naive implementation of YOLO but I wasn't aware about retro-reflections. The papers he linked in the GH discuss very interesting ideas