Test-time scaling (TTS) improves generative models by allocating additional inference-time computation to sampling, search, and verification. Existing image generation approaches, however, typically rank completed images or image-centric intermediate states. We study TTS for parallel multimodal discrete diffusion langu...
Ya-Song Dai, Zeeshan Hayder, David Ahmedt-Aristizabal et al.· 0 citations
We present an inference-time approach that enhances the visual diversity of image sets generated by text-to-image models, without compromising the average image quality. Our method leverages the intrinsic geometry of hidden representations in pretrained models to diversify images generated from the same prompt. First,...
Qing-Tao Yu, Zhen Wang, N. Lai et al.· 0 citations
D$^3CL is established as a parameter-efficient adaptation framework for pretrained diffusion models, showing that noise-level contrastive learning can structure denoising representations for discriminative tasks while maintaining generative performance.
Yasong Dai, Zeeshan Hayder, David Ahmedt-Aristizabal et al.· arXiv.org· 0 citations
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