1 citations · 1 across the 3 of their papers we have counts for
4 papers
TMPDiff: Temporal Mixed-Precision for Diffusion Models
Basile Lewandowski, Simon Kurz, Aditya Shankar +3
Diffusion models are the go-to method for Text-to-Image generation, but their iterative denoising processes has high inference latency. Quantization reduces compute time by using l…
Match & Choose: Model Selection Framework for Fine-tuning Text-to-Image Diffusion Models
Basile Lewandowski, Robert Birke, Lydia Y. Chen
Text-to-image (T2I) models based on diffusion and transformer architectures advance rapidly. They are often pretrained on large corpora, and openly shared on a model platform, such…
MPQ-Diff: Mixed Precision Quantization for Diffusion Models
Rocco Manz Maruzzelli, Basile Lewandowski, Lydia Y. Chen
Diffusion models (DMs) generate remarkable high quality images via the stochastic denoising process, which unfortunately incurs high sampling time. Post-quantizing the trained diff…
Memory Efficient Mixed-Precision Optimizers
Basile Lewandowski, Atli Kosson
Traditional optimization methods rely on the use of single-precision floating point arithmetic, which can be costly in terms of memory size and computing power. However, mixed prec…