FIFO-Diffusion: Generating Infinite Videos from Text without Training
FIFO-Diffusion leverages pretrained diffusion models with diagonal denoising, latent partitioning, and lookahead to generate infinite long videos without extra training.
Key Findings
Methodology
This approach utilizes a pretrained diffusion model, employing diagonal denoising, latent partitioning, and lookahead techniques. It maintains a FIFO queue of frame latents, progressively denoising the front frame while enqueuing new noise at the rear. Latent partitioning reduces the training-inference gap by dividing the queue into segments, enabling finer inference steps. Lookahead denoising allows frames to reference cleaner, earlier frames, enhancing temporal coherence. The method supports parallel GPU inference, with constant memory regardless of video length, enabling the generation of arbitrarily long videos without retraining.
Key Results
- On multiple baselines like VideoCrafter2, the method generates videos exceeding 10,000 frames with high visual quality, maintaining scene dynamics and semantic consistency. Quantitative metrics show FVD of 596.64 and IS of 74.44, outperforming prior techniques.
- In ablation studies, latent partitioning and lookahead denoising significantly improved temporal stability and detail preservation, verified through user preference tests.
- The approach achieves efficient parallel inference with fixed memory footprint, demonstrating scalability for long video synthesis on multi-GPU setups.
Significance
This work addresses the fundamental challenge of generating infinitely long videos with high fidelity without additional training. It introduces a novel queue-based diagonal denoising framework that preserves long-term context, overcoming limitations of chunked autoregressive methods. Its efficiency and scalability open new avenues for real-time virtual content creation, immersive media, and entertainment industries, pushing the boundaries of AI-generated video content.
Technical Contribution
The paper introduces a training-free, queue-based diagonal denoising mechanism integrated with latent space partitioning and lookahead strategies. These innovations reduce the training-inference gap, enable parallel processing, and support indefinite sequence generation. Theoretical guarantees on error bounds are provided, and the method demonstrates broad compatibility with existing pretrained diffusion models, marking a significant step forward in long sequence diffusion-based generation.
Novelty
This is the first approach to enable training-free, infinitely long video generation using a queue-based diagonal denoising process. It uniquely combines latent space partitioning and lookahead denoising, addressing the core issues of long-term consistency and computational scalability, setting it apart from prior autoregressive or hierarchical methods.
Limitations
- The method may still face challenges in extremely complex or highly dynamic scenes, where content degradation or motion blur can occur over very long sequences. It relies on pretrained short-video models, limiting generalization to new domains.
- Additional computational overhead from latent partitioning and lookahead denoising exists, especially in real-time scenarios. Further optimization is needed for deployment in resource-constrained environments.
- Current approach primarily focuses on visual coherence; integrating multimodal cues like audio or text for richer content remains future work.
Future Work
Future research will explore adaptive queue management, multi-modal integration, and dynamic scene understanding to further improve long-term coherence and content diversity. Extending the framework to incorporate user control and real-time editing capabilities will broaden practical applications, aiming for fully autonomous, infinite video generation systems.
AI Executive Summary
The advent of diffusion models has revolutionized image synthesis, yet extending this success to long video sequences remains a formidable challenge. Traditional methods rely heavily on training complex models or autoregressive prediction, which often suffer from error accumulation, content inconsistency, and high computational costs. Addressing these issues, this paper introduces FIFO-Diffusion, a novel inference technique that leverages pretrained diffusion models to generate arbitrarily long videos without additional training. Its core innovation lies in a queue-based diagonal denoising mechanism, where a fixed-length sequence of frame latents undergoes iterative noise removal, with the oldest frame being dequeued and a new noise frame enqueued at each step. To further enhance quality, the authors incorporate latent space partitioning, reducing the noise level gap between frames, and lookahead denoising, enabling frames to reference cleaner, earlier frames. These strategies collectively improve temporal coherence, scene consistency, and detail preservation, while maintaining constant memory usage regardless of video length. Extensive experiments on models like VideoCrafter2, Zeroscope2, and Open-Sora demonstrate the method’s ability to produce high-quality videos exceeding 10,000 frames, with natural motion and semantic fidelity. Quantitative metrics such as FVD and IS surpass prior approaches, and user studies confirm superior perceptual quality. The approach’s scalability and efficiency, supported by multi-GPU parallelization, make it a promising solution for real-time, infinite video generation. This work paves the way for future innovations in AI-driven content creation, with potential applications spanning virtual reality, entertainment, and interactive media. Challenges remain in handling highly dynamic scenes and multimodal integration, but the foundation laid here offers a significant leap toward autonomous, limitless video synthesis.
Deep Analysis
Background
视频生成技术经历了从基于GAN的早期方法到扩散模型的崛起。早期作品如VGAN、TGAN在短视频中取得一定成功，但难以扩展到长序列。近年来，扩散模型如DDPM、VideoLDM和Make-A-Video在图像和短视频生成中表现优异。它们通过逐步去噪实现高质量内容，但受限于模型参数和计算成本，难以生成无限长的视频。长视频生成的关键难题在于内容连续性和运动一致性，传统自回归和分块策略在保持长序列的连贯性方面存在缺陷。本文提出利用预训练模型，通过队列式对角去噪机制突破这一瓶颈，支持无限长视频的连续生成。
Core Problem
现有长视频生成方法多依赖训练复杂模型或逐帧预测，导致内容退化、运动模糊和信息丢失。尤其在极长序列中，模型难以保持场景的连贯性和细节丰富性，错误累积严重限制了实际应用。传统自回归方法计算成本高，难以扩展到无限长视频。如何在保证内容质量的同时，实现无限长视频的连续生成，成为行业亟待解决的难题。
Innovation
本研究提出FIFO-Diffusion，核心创新包括：1）队列式对角去噪，支持连续帧逐步去噪，保持长时间序列的内容一致性；2）潜空间划分，缩小训练-推理差距，提升生成质量；3）前瞻去噪，增强帧间参考信息，改善动态表现。这些创新结合预训练模型，避免额外训练成本，支持多GPU并行，极大提升了长视频生成的效率和质量。
Methodology
- �� 采用预训练扩散模型作为基础，利用队列存储连续帧的潜在表示。
- �� 实现对角线去噪，逐步去除队列中最前端帧的噪声，同时在队列尾部加入新随机噪声帧。
- �� 通过潜空间划分，将队列分为多个块，分别进行去噪，减小训练-推理差距。
- �� 引入前瞻去噪机制，使后续帧参考更清晰的前置帧，提升动态一致性。
- �� 支持多GPU并行处理，优化推理速度和内存使用。
- �� 不依赖额外训练，直接在预训练模型上实现无限长视频生成。
Experiments
在VideoCrafter2、Zeroscope2、Open-Sora等多个基线模型上进行测试，使用FVD和IS作为评价指标。生成超过1万帧长视频，观察内容一致性和运动自然性。通过AB测试和用户偏好调查，验证方法优越性。参数设置包括64步DDIM采样，潜空间划分为4块，结合前瞻去噪提升质量。对比传统自回归和chunked方法，展示了显著优势。
Results
实验显示FIFO-Diffusion在FVD达596.64，IS达74.44，明显优于现有长视频生成技术。长达10,000帧的视频内容保持高度连贯，运动自然，场景丰富。引入潜空间划分和前瞻去噪后，视频质量和稳定性进一步提升，用户偏好显著增强。多GPU并行实现使得推理速度大幅提高，内存消耗保持不变，极大改善了可扩展性。
Applications
该技术可广泛应用于虚拟现实、动画制作、内容创作等领域，尤其适合生成大规模、动态丰富的虚拟场景。无需额外训练，降低了技术门槛，便于行业快速部署。未来结合多模态信息，将推动智能内容生成的边界，满足个性化、实时化的需求。
Limitations & Outlook
当前模型在极端复杂场景或极长时间跨度中仍可能出现内容退化或运动模糊。潜空间划分虽提升效率，但增加了推理复杂度。模型依赖预训练短视频，泛化能力有限。未来需解决多模态融合、内容多样性和动态调节等挑战，以实现更广泛的应用。
Plain Language Accessible to non-experts
想象你在厨房做饭，准备一份大餐。每次你只做一小部分菜，但你用一个特殊的锅，把每一道菜都放进去，然后用锅里的热气让它们变成熟。这个锅就像队列，把每一部分菜按顺序处理好，再依次取出。每次你做完一部分，就把新的食材放进去，确保每道菜都能连续做出来。这样，即使你只准备了少量原料，也能做出一份长长的、丰富的菜肴。FIFO-Diffusion也是这样，它用一个“队列”逐步去噪每一帧画面，保证视频连续、自然，不需要提前训练一堆模型，就能做出无限长的精彩视频，非常酷！
ELI14 Explained like you're 14
你知道拍电影的时候，导演会拍很多场景，然后把它们拼在一起，形成一部长片吗？但如果每次只拍一小段，想拼成一部长片就很难保证内容连贯。FIFO-Diffusion就像一个神奇的拼接机，它可以用已经训练好的“魔法”模型，连续不断地生成画面，不管多长都不怕。它用一个“队列”把每一帧画面存起来，然后逐步去掉噪点，让画面变得清晰。每次生成新画面时，它会参考前面的画面，让动作和场景看起来很自然。这样就可以不用重新训练模型，也能做出长长的电影一样的视频，非常酷！
Abstract
We propose a novel inference technique based on a pretrained diffusion model for text-conditional video generation. Our approach, called FIFO-Diffusion, is conceptually capable of generating infinitely long videos without additional training. This is achieved by iteratively performing diagonal denoising, which simultaneously processes a series of consecutive frames with increasing noise levels in a queue; our method dequeues a fully denoised frame at the head while enqueuing a new random noise frame at the tail. However, diagonal denoising is a double-edged sword as the frames near the tail can take advantage of cleaner frames by forward reference but such a strategy induces the discrepancy between training and inference. Hence, we introduce latent partitioning to reduce the training-inference gap and lookahead denoising to leverage the benefit of forward referencing. Practically, FIFO-Diffusion consumes a constant amount of memory regardless of the target video length given a baseline model, while well-suited for parallel inference on multiple GPUs. We have demonstrated the promising results and effectiveness of the proposed methods on existing text-to-video generation baselines. Generated video examples and source codes are available at our project page.