Hi everyone! I hope this message finds you well! Wow, it has been a while since my last post here 😅 I am doing alright, no worries. This is actually a "Good News" post. I tried building some GPTs on the new OpenAI store and realized that they don't have even the most basic analytics to measure engagement with your creations, which is frustrating if you want to grow your GPTs. ✨Here is the good new…

#87: "Hierarchical Text-Conditional Image Generation with CLIP Latents" by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu et al. explained in 5 minutes As always, here is the visual summary! (All figures are taken from the original paper) *** Tip Casual GAN Papers on KoFi to help this community grow!

#87: "Hierarchical Text-Conditional Image Generation with CLIP Latents" by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu et al. explained in 5 minutes Does this paper even need an introduction? This is DALL-E 2 and if you have somehow not heard of it yet, sit down. Your mind is about to be blown. Enough said. Let’s dive in, shall we? https://www.casualganpapers.com/sota-text-to-image-op…

Hey everyone, It has been a while! I was busy with a NeurIPS submission 👀 but I think I am back now. Lots of papers to cover, lots of posts to write, lots of people to meet! Let's start this off with a bang that is DALL-E 2 💥 Surprisingly well written, less wordy than StyleGAN 3, and an elegant idea as long as you pretend that the diffusion math doesn't exist 🤡 Best, -Kirill

#86.4: "High-Resolution Image Synthesis with Latent Diffusion Models” by Robin Rombach, Andreas Blattmann et al. As always, here is the visual summary! (All figures are taken from the original paper) *** Tip Casual GAN Papers on KoFi to help this community grow!