@thesis{Rampas2024, author = {Rampas, Dominic Antonio}, title = {On neural compression using diffusion autoencoders}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:573-44286}, pages = {65}, school = {Technische Hochschule Ingolstadt}, year = {2024}, abstract = {This work presents an examination of using diffusion models to achieve efficient data compression. As diffusion models have become more dominant in areas such as generative modelling, this work shows that they achieve a strong performance on the task of image-, and video-compression and outperform classical algorithms, as well as established neural compression algorithms. This type of model, referred to as Diffusion Autoencoders, is able to achieve spatial compression factors of 32x, while achieving high quality reconstructions that even maintain fine details of the input. The bachelor thesis presents a thorough analysis of individual components of the diffusion autoencoder through extensive experiments, while undermining the solidness of the approach by numerous comparisons and ablation studies. Additionally, an in-depth overview of related work for classical-, and neural-compression, diffusion models and evaluation methods is given.}, language = {en} } @inproceedings{PerniasRampasRichteretal.2024, author = {Pernias, Pablo and Rampas, Dominic and Richter, Mats Leon and Pal, Christopher and Aubreville, Marc}, title = {W{\"u}rstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models}, booktitle = {The Twelfth International Conference on Learning Representations (ICLR 2024)}, publisher = {OpenReview}, url = {https://openreview.net/forum?id=gU58d5QeGv}, year = {2024}, language = {en} }