Paper 2605.18233
Enhancing Train-Free Infinite-Frame Generation for Consistent Long Videos
- Published
- May 2026
- Research lab
- Independent
- Citations
- 1
- GitHub
- Not linked
01 In brief
Summary
The paper introduces MIGA, a training-free method for infinite-frame long video generation that builds on frame-level autoregressive frameworks like FIFO-Diffusion.
MIGA addresses two key limitations: the training-inference gap and long-term consistency.
It proposes a Two-Stage Training-Inference Alignment (TTA) mechanism that reduces the noise span of input latents via zigzag iterative denoising (Stage 1) and unified noise-level denoising (Stage 2).
Additionally, a Dual Consistency Enhancement (DCE) mechanism uses self-reflection to correct early high-noise latents and long-range frame guidance to incorporate low-noise latents for improved temporal consistency.
Experiments on VBench and NarrLV show MIGA achieves state-of-the-art performance, with significant gains in subject and background consistency over FIFO-Diffusion.
The method supports multi-prompt control and generates videos of 1000+ frames with constant memory consumption.
02 From the paper
Abstract
Without incurring significant computational overhead, train-free long video generation aims to enable foundation video generation models to produce longer videos. Frame-level autoregressive frameworks, e.g., FIFO-diffusion, offer the advantage of generating infinitely long videos with constant memory consumption. However, the mismatch between training and inference, coupled with the challenge of maintaining long-term consistency, limits the effective utilization of foundation models. To mitigate these concerns, we propose \textbf{MIGA}, a novel infinite-frame long video generation method. Firstly, we propose an effective two-stage alignment mechanism that mitigates the training-inference gap by reducing the excessive noise span fed to the model. We then introduce an innovative dual consistency enhancement mechanism, where the self-reflection approach corrects early high-noise frames and the long-range frame guidance approach leverages later low-noise frames with broad coverage to steer generation, jointly improving temporal consistency. Extensive experiments on VBench and NarrLV demonstrate the state-of-the-art performance of MIGA. Our project page is available at https://xiaokunfeng.github.io/miga_homepage/.