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Project: Sustainable Diffusion Models


Project description:

Diffusion Models are deep-learning models that achieve state-of-the-art performance in many image synthesis tasks. They are typically parameterized with UNets and consist of billions of weights. Expressing their weights in float32 leads to models that  cannot be easily deployed on edge devices (e.g., smartphones) since they take too much space and consume too much energy. Thus, the problem is to develop and/or apply techniques like quantization-aware training (QAT), post-training quantization (PTQ), or neural architecture search for obtaining sustainable diffusion models.

In this thesis: (a) you will study the techniques for formulating more sustainable diffusion models, (b) you will formulate and code your own sustainable diffusion models, (c) you will design and carry out evaluations for your sustainable diffusion models.

Literature (examples):


  • reading and understanding scientific literature
  • very good coding skills in Python using PyTorch and other ML libraries
  • good knowledge of Deep Learning and the basics of Generative AI
  • curious attitude, independence, thinking out-of-the box
Jakub Tomczak
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