Lin Zhang

Lin Zhang, Sicheng Mo, Zefan Cai et al.

Autoregressive video diffusion models have emerged as a promising approach for long video generation, achieving strong performance in streaming settings. However, existing methods are restricted to forward temporal generation, whereas practical video creation often requires flexible generation order, e.g., conditioning on future context to extend backward, or on both past and future context for inbetween generation. We bridge this gap by training an autoregressive model that supports generation in arbitrary temporal directions. A key technical challenge arises from the Causal 3D VAE widely used in video diffusion models, which encodes latents strictly conditioned on past context. While suited for forward generation, this causal structure causes inter-block discontinuities when generation proceeds backward. To address this, we introduce blockwise anchor latents, a set of auxiliary latents that restore the missing past context at block boundaries during backward generation. Built on this design, we propose UniTemp, a bidirectional distillation framework that trains a single autoregressive student model for any-direction video generation. At inference time, UniTemp conditions on arbitrary past and/or future frames, improving controllability for both bidirectional and inbetween generation. Experiments show that UniTemp maintains competitive performance on short and long video generation compared to forward-only methods, while enabling diverse workflows such as bidirectional video extension, inbetween generation, looping video generation, scene transition, and visual story generation. Project website: https://lzhangbj.github.io/projects/unitemp/

Boran Sun, Guoyong Jiang, Lin Zhang et al.

Diffusion models are now a dominant approach for high-fidelity image and video generation, yet scaling their training across GPU clusters remains challenging. Unlike transformer-only architectures, diffusion backbones commonly adopt UNet-style encoder-decoder structures with heterogeneous layers and long-range skip connections. Under conventional pipeline parallelism, these non-local dependencies force large skip activations and their gradients to traverse multiple pipeline boundaries, making peer-to-peer (P2P) communication a dominant bottleneck and substantially reducing pipeline efficiency. In this paper, we present PULSE, an automatic pipeline-parallel training strategy that makes skip locality a first-class optimization objective. PULSE eliminates skip-induced communication by collocating skip-connected encoder-decoder layers on the same device and caching skip activations locally for later use in backpropagation. To realize this placement while maintaining high pipeline utilization, PULSE co-designs: (1) a skip-aware dynamic-programming partitioner that balances heterogeneous stage workloads under symmetric collocation constraints, (2) an ILP-based schedule synthesizer that generates bubble-efficient wave schedules for the resulting stage-to-device mapping, and (3) a hybrid parallelism tuner that selects pipeline/data-parallel degrees and microbatch sizes under memory and network constraints. Our extensive experiments show that the volume of communication can be reduced by 89 percent, and the training throughput can be increased by up to 2.3x on communication-bound hardware, compared with state-of-the-art parallelism strategies.