4 papers
Lookahead Unmasking Elicits Accurate Decoding in Diffusion Language Models
Sanghyun Lee, Seungryong Kim, Jongho Park +1
Masked Diffusion Models (MDMs) as language models generate by iteratively unmasking tokens, yet their performance crucially depends on the inference time order of unmasking. Prevai…
Effective Test-Time Scaling of Discrete Diffusion through Iterative Refinement
Sanghyun Lee, Sunwoo Kim, Seungryong Kim +2
Test-time scaling through reward-guided generation remains largely unexplored for discrete diffusion models despite its potential as a promising alternative. In this work, we intro…
Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models
Donghoon Ahn, Jiwon Kang, Sanghyun Lee +7
Recent guidance methods in diffusion models steer reverse sampling by perturbing the model to construct an implicit weak model and guide generation away from it. Among these approa…
A Noise is Worth Diffusion Guidance
Donghoon Ahn, Jiwon Kang, Sanghyun Lee +9
Diffusion models excel in generating high-quality images. However, current diffusion models struggle to produce reliable images without guidance methods, such as classifier-free gu…