8 papers
Flowception: Temporally Expansive Flow Matching for Video Generation
Tariq Berrada Ifriqi, John Nguyen, Karteek Alahari +2
We present Flowception, a novel non-autoregressive and variable-length video generation framework. Flowception learns a probability path that interleaves discrete frame insertions…
TV2TV: A Unified Framework for Interleaved Language and Video Generation
Xiaochuang Han, Youssef Emad, Melissa Hall +15
Video generation models are rapidly advancing, but can still struggle with complex video outputs that require significant semantic branching or repeated high-level reasoning about…
Entropy Rectifying Guidance for Diffusion and Flow Models
Tariq Berrada Ifriqi, Adriana Romero-Soriano, Michal Drozdzal +2
Guidance techniques are commonly used in diffusion and flow models to improve image quality and input consistency for conditional generative tasks such as class-conditional and tex…
Increasing the Utility of Synthetic Images through Chamfer Guidance
Nicola Dall'Asen, Xiaofeng Zhang, Reyhane Askari Hemmat +4
Conditional image generative models hold considerable promise to produce infinite amounts of synthetic training data. Yet, recent progress in generation quality has come at the exp…
Improving the Scaling Laws of Synthetic Data with Deliberate Practice
Reyhane Askari-Hemmat, Mohammad Pezeshki, Elvis Dohmatob +6
Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a novel framework that improves sample effici…
On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models
Tariq Berrada Ifriqi, Pietro Astolfi, Melissa Hall +8
Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, the key components of the best performing LDM training recipe…