by America Ortiz | Apr 23, 2025 | Machine Learning
Revamping Diffusion Guidance for Conditional and Unconditional Generation In this paper, we revisit the CFG update rule and introduce modifications to address this issue. We first decompose the update term in CFG into parallel and orthogonal components with respect to...
by America Ortiz | Apr 23, 2025 | Machine Learning
No Training, No Problem: Rethinking Diffusion Guidance for Diffusion Models In this paper, we revisit the core principles of CFG and introduce a new method, independent condition guidance (ICG), which provides the benefits of CFG without the need for any special...
by America Ortiz | Apr 8, 2025 | Rendering, Video Processing, Visual Computing
Volume Scattering Probability Guiding We demonstrate that direct control over the VSP can significantly improve efficiency and present an unbiased volume rendering algorithm based on an existing resampling framework for precise control over the VSP. October 7, 2024...
by America Ortiz | Feb 27, 2025 | Rendering, Video Processing, Visual Computing
CLIP-Fusion: A Spatio-Temporal Quality Metric for Frame Interpolation In this paper, we aim to leverage semantic feature extraction capabilities of the pre-trained visual backbone of CLIP. Specifically, we adapt its multi-scale approach to our feature extraction...
by America Ortiz | Dec 10, 2024 | Machine Learning
LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models In this paper, we introduce LiteVAE, a new autoencoder design for LDMs, which leverages the 2D discrete wavelet transform to enhance scalability and computational efficiency over...
