Archived
2026-09-29Unifying Distributional Training for One-Step Visual Generation
Chi Zhang, Haoyang Shi, Yueyi Liu, Ruichuan An, Junkang Zhou, Chang Li, et al.
arXiv:2609.35763v1Today’s pick is MGFlow, a new framework for one-step visual generation. The problem it tackles is simple to state but hard to solve: how do you generate a high-quality image in a single pass, without the multiple refinement steps used by diffusion models, and without collapsing into repetitive or blurry outputs? MGFlow starts from a useful idea called distributional training, where a model learns by matching the statistics of real and generated features in a frozen representation space. The authors then build a unified theory that connects several existing methods and turn it into a practical model based on Gaussian mixtures. This lets the system represent image features at a flexible level of detail, from broad global structure down to finer sample-level behavior. The key result is that MGFlow can produce strong one-step images while avoiding mode collapse. It also works well as a post-training upgrade for text-to-image systems, making it broadly relevant for faster, cheaper generation.
Also spotted that day
FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur DatasetsTelescopic Language ModelsPDMD: Projected Distribution Matching Distillation for Video Diffusion ModelsLearning Native Reflection in Unified Models with Interleaved Reinforcement LearningRetrieving Biblical Intertextual References in Karen Blixen's Seven Gothic TalesScaling Long-Form Story Generation via Narrative State TrackingTokenCast: Forecasting Token Consumption During LLM Agent ExecutionStatistical Learning of Contractive Dynamical Representations for Composite Adaptive ControlNeural Harmonic Measure OperatorHow to Loop MoE: Flatten the Experts, Untie the AttentionKV-streams for Efficient Compaction in Agentic Reinforcement LearningTowards Communication-Efficient Social Intelligence in Language AgentsImproving Test-Time Scaling with Adaptive Looped TransformersCopy the Same, Distill the Difference: Initializing Linear Vision Transformers
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