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The paper presents Improved Distributional Diffusion Models (iDDM), which overcome scaling limits of DDMs via late particle expansion and time-dependent scoring rule schedules, achieving 4.48 FID at 4 steps and 2.38 FID at 50 steps on ImageNet-256 with DiT-XL/2 from a single-stage from-scratch training without teachers or self-distillation.
LingBot-Video presents a DiT-based video pretraining framework with Mixture-of-Experts architecture, specialized data augmentation, and multi-dimensional reward system for embodied intelligence applications.
PhysisForcing is a training framework that enhances embodied video generation for robotic manipulation by enforcing physical consistency through pixel-level trajectory alignment and semantic-level relational alignment losses in a DiT-based architecture, achieving notable improvements on benchmarks.
ByteDance open-sourced Bernini-R, a video diffusion renderer that combines an MLLM-based semantic planner with a DiT-based renderer for unified video generation and editing, achieving top-tier performance on video editing.
Baidu releases ERNIE-Image-Turbo, a distilled text-to-image generation model that achieves fast generation in 8 inference steps while maintaining strong text rendering, instruction following, and structured image generation capabilities.