Filtered to diffusion models · clear filter
This paper investigates how to improve the adaptation of diffusion models in a way that doesn't rely on a classifier, and how to address a problem where the model can't accurately learn from its teacher. Practitioners might care about this because it could lead to more effective knowledge transfer in machine learning applications.
This paper investigates how Diffusion Language Models (DLMs) internally represent time and how this representation can be used to modulate the model's behavior. Practitioners might care because understanding how DLMs process time could lead to more controllable and interpretable models.
This paper investigates how text conditioning affects visual generation and proposes ways to improve it, leading to better performance on various benchmarks. Practitioners might care about the findings to develop more effective text-to-image models.
This paper introduces Parallel Decoding Distillation (PDD), a new method to speed up image and video generation in diffusion and flow models, achieving state-of-the-art performance while improving video diversity. Practitioners might care about PDD's potential to accelerate video generation tasks.
This paper develops a new method for editing videos with explicit layered representations, which allows for more realistic compositing, object reuse, and manipulation. Practitioners working on video editing and compositing might care about this research as it could lead to improved video editing tools.