Exploring and experimenting with OpenAI unconditional diffusion models, mainly using Disco Diffusion. Thank you to the DElab of the Hasso Plattner Institute for letting me use the power of a few A100 GPUs. It was incredibly interesting to learn more and more of how the AI interprets the input, both the prompt as well as all the other parameters, and getting better at formulating of what I want the AI to do. In the midst of exploring, I followed the development of StabilityAI's Stable Diffusion, which blew these models out of the water in terms of coherency and speed. Feel free to see my experiments with Stable Diffusion here →
You may notice the number of iterations for each image; to get the results I had in mind, I had to fiddle around a lot with the parameters of the generation as well as the prompt formulation. Nth iteration means that this was the nth image generated from the same conceptual idea. If you are interested in the nitty gritty details, an example prompt can be found at the bottom of this page.
