Text2Img: Recent Trends Learning Materials If you are like me how have read all the hype about text2img generation but did not have time ti dive into the models' details, I prepared the list of sources to learn the recent advantages of the topic. Estimation of time to go through all of it: 2-3 evenings. * [link] Amazing visualization of ALL models and papers that made text2img so cool as it is now: image detection -> fake image generation -> style transfer -> text2img. Really helps to track the history and understand the reason why it is going as it is going. * [link] Series of lectures from Fast.ai about Stable Diffusion. I really recommend to at least watch the first lecture (time code included) — it is enough to understand the main idea. Additionally, github repo with tutorials. * [link] After general idea, it is worth to watch CLIP paper analysis. * [link] Of course, thanks to Jay Alammar, we also have The Illustrated Stable Diffusion. * [link] The biggest dive into the code: The Annotated Diffusion Model (colab). Enjoy💪 P.S. There is a chance that I will create 1-2 lectures based on this material for TUM NLP course. Let me know, it you are interested in the recording. #edu #text2img