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cs.CV2026

HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing

Haoran You, Yotam Nitzan, Lingzhi Zhang +7

Creative image editing tools, such as Photoshop's Remove or Generative Fill buttons, are central to everyday customer use and account for a major share of traffic in Photoshop and…

cs.CV2026

DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization

Zihan Ding, Chi Jin, Difan Liu +6

Diffusion probabilistic models have shown significant progress in video generation; however, their computational efficiency is limited by the large number of sampling steps require…

cs.CV2025

Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks

Bhishma Dedhia, David Bourgin, Krishna Kumar Singh +5

Diffusion Transformers (DiTs) can generate short photorealistic videos, yet directly training and sampling longer videos with full attention across the video remains computationall…

cs.CV2025

Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers

Haoran You, Connelly Barnes, Yuqian Zhou +10

Diffusion Transformers (DiTs) have achieved state-of-the-art (SOTA) image generation quality but suffer from high latency and memory inefficiency, making them difficult to deploy o…

cs.CV2024

Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection

Alireza Ganjdanesh, Yan Kang, Yuchen Liu +3

Diffusion probabilistic models can generate high-quality samples. Yet, their sampling process requires numerous denoising steps, making it slow and computationally intensive. We pr…

cs.CV2024

SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model

Zhengang Li, Yan Kang, Yuchen Liu +4

While AI-generated content has garnered significant attention, achieving photo-realistic video synthesis remains a formidable challenge. Despite the promising advances in diffusion…