10 papers
HierarchicalPrune: Position-Aware Compression for Large-Scale Diffusion Models
Young D. Kwon, Rui Li, Sijia Li +3
State-of-the-art text-to-image diffusion models (DMs) achieve remarkable quality, yet their massive parameter scale (8-11B) poses significant challenges for inferences on resource-…
FraQAT: Quantization Aware Training with Fractional bits
Luca Morreale, Alberto Gil C. P. Ramos, Malcolm Chadwick +4
State-of-the-art (SOTA) generative models have demonstrated impressive capabilities in image synthesis or text generation, often with a large capacity model. However, these large m…
Efficient High-Resolution Image Editing with Hallucination-Aware Loss and Adaptive Tiling
Young D. Kwon, Abhinav Mehrotra, Malcolm Chadwick +2
High-resolution (4K) image-to-image synthesis has become increasingly important for mobile applications. Existing diffusion models for image editing face significant challenges, in…
EDiT: Efficient Diffusion Transformers with Linear Compressed Attention
Philipp Becker, Abhinav Mehrotra, Ruchika Chavhan +5
Diffusion Transformers (DiTs) have emerged as a leading architecture for text-to-image synthesis, producing high-quality and photorealistic images. However, the quadratic scaling p…
Benchmarking Rotary Position Embeddings for Automatic Speech Recognition
Shucong Zhang, Titouan Parcollet, Rogier van Dalen +1
Self-attention relies on positional embeddings to encode input order. Relative Position (RelPos) embeddings are widely used in Automatic Speech Recognition (ASR). However, RelPos h…
Robust Unsupervised Adaptation of a Speech Recogniser Using Entropy Minimisation and Speaker Codes
Rogier C. van Dalen, Shucong Zhang, Titouan Parcollet +1
Speech recognisers usually perform optimally only in a specific environment and need to be adapted to work well in another. For adaptation to a new speaker, there is often too litt…