Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
Proposes Stable Video Diffusion with a three-stage training strategy, leveraging large-scale curated datasets to significantly improve high-resolution video generation.
Key Findings
Methodology
This work employs a three-stage training pipeline: first, text-to-image pretraining using Stable Diffusion 2.1; second, large-scale low-resolution video pretraining on datasets like LVD-10M; third, high-resolution fine-tuning on curated high-quality videos. Data curation involves systematic filtering using multimodal signals such as CLIP embeddings and Optical Flow to remove static or low-motion clips, ensuring dynamic richness. The architecture builds on Blattmann et al.'s latent video diffusion framework, integrating temporal convolution and attention layers, combined with multimodal priors for multi-task adaptation. Experiments on datasets like WebVid-10M and LVD-10M-F demonstrate superior performance, with metrics such as FVD and user preference scores surpassing state-of-the-art models.
Key Results
- On UCF-101 zero-shot evaluation, the model achieves an FVD of 242.02, outperforming CogVideo and Make-A-Video. Human preference studies favor the proposed model over existing baselines.
- Fine-tuned at 576×1024 resolution, the model produces high-quality videos that are preferred by users over closed-source models like GEN-2 and PikaLabs.
- The model exhibits strong multi-view and motion understanding, enabling multi-view consistent generation and 3D priors, outperforming traditional image-based methods with approximately half the computational cost.
Significance
This research addresses the critical bottleneck of data quality and scale in video generation. By establishing a systematic data curation and training framework, it significantly enhances the realism, diversity, and temporal coherence of generated videos. The model's multi-task capabilities, including multi-view synthesis and motion control, open new avenues for applications in virtual reality, film production, and content creation. It demonstrates that large-scale curated datasets combined with advanced architecture design can push the boundaries of high-resolution, real-time video synthesis, setting a new standard in the field.
Technical Contribution
The core innovation lies in the systematic three-stage training approach, integrating large-scale data curation, multimodal filtering, and multi-task fine-tuning. The architecture extends the latent diffusion framework with temporal convolution and attention modules, enhancing temporal consistency. The introduction of multimodal priors like CLIP and Optical Flow enables effective data filtering and multi-view generation. Additionally, the use of LoRA modules for camera motion control exemplifies flexible adaptation. These contributions collectively improve the model's capacity for high-fidelity, multi-task video synthesis, with demonstrated superiority over prior methods.
Novelty
This study is the first to systematically combine large-scale data curation with multi-stage training for latent video diffusion models, significantly boosting performance. It introduces multi-view 3D priors into diffusion-based video synthesis, enabling multi-angle consistent outputs. Unlike previous works that rely on limited datasets or architecture modifications alone, this approach leverages extensive multimodal filtering and fine-tuning, establishing a new benchmark for high-resolution, multi-task video generation.
Limitations
- The model heavily depends on large, high-quality datasets, making data collection and filtering resource-intensive.
- Inference speed remains relatively slow, limiting real-time applications.
- Handling extremely complex or fast-moving scenes still poses challenges for maintaining temporal coherence.
Future Work
Future directions include optimizing data collection pipelines for efficiency, accelerating inference through model compression or distillation, and enhancing dynamic scene understanding. Exploring reinforcement learning for motion control and expanding multi-view capabilities to more complex 3D environments are also promising avenues.
AI Executive Summary
Recent advances in generative modeling have revolutionized image synthesis, yet high-quality, high-resolution video generation remains a complex challenge. Existing approaches often struggle with maintaining temporal coherence, detail richness, and multi-view consistency, especially at scale. This paper introduces Stable Video Diffusion, a novel framework that systematically addresses these issues through a three-stage training strategy. The first stage leverages pretraining on large-scale text-to-image datasets like Stable Diffusion 2.1, establishing a strong visual foundation. The second stage involves pretraining on massive, curated, low-resolution video datasets such as LVD-10M, where multimodal filtering techniques like CLIP embeddings and Optical Flow are employed to ensure dynamic richness and remove static or low-motion clips. The third stage fine-tunes the model on high-quality, high-resolution videos, significantly improving detail and temporal coherence.
The architecture builds upon Blattmann et al.'s latent video diffusion model, incorporating temporal convolution and attention layers to enhance temporal consistency. Additionally, multimodal priors enable multi-task learning, including text-to-video, image-to-video, and multi-view synthesis. The model's performance is validated on datasets like WebVid-10M and UCF-101, where it surpasses existing methods with a FVD of 242.02 and user preference scores favoring the proposed approach. Notably, the model demonstrates strong multi-view and motion understanding, enabling multi-angle consistent outputs and 3D scene priors, which outperform traditional image-based methods while reducing computational costs by approximately 50%.
This work has profound implications for virtual reality, film production, and content creation, offering a scalable, high-fidelity solution for automatic video generation. Despite its success, challenges remain in data collection, inference speed, and handling complex dynamic scenes. Future research will focus on optimizing these aspects, exploring reinforcement learning for motion control, and expanding multi-view capabilities to more complex 3D environments, paving the way for more immersive and realistic virtual experiences.
Deep Analysis
Background
近年来，深度学习推动了视频生成技术的快速发展。早期方法多依赖GAN或自回归模型，但在高分辨率和动态一致性方面存在局限。扩散模型作为新兴技术，已在图像生成中取得突破，代表性工作如Denoising Diffusion Probabilistic Models (DDPM)和Stable Diffusion。视频生成方面，研究者尝试在图像基础上加入时间模块，提升连续性。Blattmann等提出的潜在视频扩散模型（Video-LDM）结合潜在空间与扩散机制，为高效生成提供新思路。尽管如此，数据质量和规模仍是瓶颈，如何系统采集、筛选大规模高质量视频数据，成为提升模型性能的关键。此前WebVid-10M等数据集虽广泛使用，但存在水印和低动态丰富度的问题。多模态信息（如CLIP、Optical Flow）在视频理解中的应用逐渐成熟，为数据筛选提供技术支撑。整体来看，视频生成技术正朝着高分辨率、多任务、多模态方向发展，但仍需突破数据瓶颈与模型效率。
Core Problem
当前视频生成模型在高质量、多样性和动态一致性方面仍存在不足。尤其是在高分辨率场景下，模型难以保持细节丰富和运动连贯，受限于训练数据的质量和规模。现有方法多依赖有限的高质量数据集，且数据采集和筛选过程繁琐，难以规模化。此外，模型在多视角、多任务场景中的表现不足，限制了其实际应用潜力。如何系统化采集大规模高质量视频数据，结合有效的训练策略，成为亟需解决的问题。这不仅关系到模型性能，也影响其在虚拟现实、影视制作等行业的落地应用。
Innovation
本研究提出了三阶段训练框架：包括文本到图像预训练、低分辨率视频预训练和高质量视频微调，系统性整合大规模数据资源。引入多模态筛选技术（如CLIP、Optical Flow）确保数据动态丰富，显著提升模型性能。架构上，结合Blattmann潜在视频扩散模型，加入时间卷积和注意力机制，增强时序一致性。创新点还在于多视角多模态先验的引入，实现多视角一致性和三维理解。微调机制（如LoRA）实现摄像机运动控制，增强模型的动态表现能力。这些创新共同推动模型在高分辨率、多任务、多模态场景中的应用潜力。
Methodology
- �� 采集大规模视频数据集（如LVD）并应用多模态过滤（CLIP、Optical Flow）筛除静态和低质视频。• 利用预训练的Stable Diffusion 2.1模型进行文本到图像预训练，奠定视觉基础。• 在大规模低分辨率视频数据上进行预训练，加入时间卷积和注意力层，增强时序一致性。• 采集高质量视频，进行微调，确保细节丰富和动态连贯。• 采用微调技术（如LoRA）实现多任务适应，包括多视角、多模态生成。• 通过多模态先验实现多视角一致性，结合摄像机运动LoRA模块，控制动态效果。• 在多个公开数据集上进行验证，优化模型参数，提升生成质量。
Experiments
在WebVid-10M、LVD-10M-F等大规模数据集上训练模型，采用FVD、用户偏好等指标进行评估。对比不同数据采集策略、模型架构和微调方法的效果。通过人类偏好测试验证模型在高分辨率视频和多视角任务中的优越性。设置不同训练轮次和微调参数，分析模型性能的变化。还进行消融实验，验证数据筛选、架构设计对性能的贡献。最终，模型在多个公开任务中表现出超越现有技术的能力。
Results
模型在UCF-101零样本任务中FVD达242.02，优于CogVideo和Make-A-Video。微调后，在576×1024分辨率下生成的视频获得用户偏好，优于闭源模型GEN-2和PikaLabs。多视角生成能力强，能同步生成多角度视图，超越Zero123XL和SyncDreamer。大规模筛选数据显著提升模型性能，训练成本降低约一半，模型在多任务场景中表现稳定，展现出强大的运动理解和3D先验能力。
Applications
该模型可广泛应用于虚拟现实、影视特效、动画制作等领域，实现高质量自动视频生成。只需提供文本描述或单帧图像，即可生成连续视频或多视角内容，极大提高内容创作效率。未来，结合实时推理和优化算法，有望实现实时视频生成，推动行业数字化转型。
Limitations & Outlook
模型依赖大量高质量数据，数据采集和筛选成本高。推理速度较慢，不适合实时应用。在极端动态或复杂场景中，生成一致性仍有待提升。未来需优化模型结构和推理效率，增强动态场景表现力。
Plain Language Accessible to non-experts
想象你在一个工厂里，工厂每天都要生产各种产品。以前，工厂的机器只能做简单的东西，不能做复杂的模型。后来，工厂引入了一种新机器，叫做‘视频制造机’，它可以根据你的指令，自动制造出各种复杂的视频，比如动画、电影场景。这个新机器需要学习怎么做这些视频，于是工厂给它提供了很多原材料——大量不同的原始视频。工厂还用一种聪明的检测方法，筛选出那些动作丰富、画质好的视频，确保机器学到的东西都是真正有用的。经过三步训练：先学会看图片，再学会拼接视频，最后在高质量视频上微调，机器变得越来越聪明。最终，这个工厂的‘视频制造机’可以根据你的描述，自动生成高清、动态连贯的视频，还能从不同角度拍摄同一场景，就像给你看多角度的3D模型一样。这项技术让电影、游戏、虚拟现实变得更容易、更真实，也让我们未来的数字世界更加丰富多彩。
ELI14 Explained like you're 14
想象你在玩一个超级厉害的画画机器人。这个机器人可以根据你说的话，画出动画视频，比如“一个小猫在草地上玩耍”。但要让它画得漂亮、动得流畅，不是那么简单。科学家们发明了一套特别的方法，让机器人先学会看图片，然后学会拼接连续的画面，最后在很多漂亮的高质量视频上微调。它们还用一些聪明的技巧，让机器人能理解动作和不同角度的视图，就像你用手机拍多角度的照片一样。这样一来，机器人就能根据你的描述，快速生成高清、连续、真实的动画视频。这个技术就像给机器人装上了“想象力”和“动手能力”，未来可以用在电影制作、虚拟现实游戏中，让虚拟世界变得更真实、更有趣。是不是很酷？
Abstract
We present Stable Video Diffusion - a latent video diffusion model for high-resolution, state-of-the-art text-to-video and image-to-video generation. Recently, latent diffusion models trained for 2D image synthesis have been turned into generative video models by inserting temporal layers and finetuning them on small, high-quality video datasets. However, training methods in the literature vary widely, and the field has yet to agree on a unified strategy for curating video data. In this paper, we identify and evaluate three different stages for successful training of video LDMs: text-to-image pretraining, video pretraining, and high-quality video finetuning. Furthermore, we demonstrate the necessity of a well-curated pretraining dataset for generating high-quality videos and present a systematic curation process to train a strong base model, including captioning and filtering strategies. We then explore the impact of finetuning our base model on high-quality data and train a text-to-video model that is competitive with closed-source video generation. We also show that our base model provides a powerful motion representation for downstream tasks such as image-to-video generation and adaptability to camera motion-specific LoRA modules. Finally, we demonstrate that our model provides a strong multi-view 3D-prior and can serve as a base to finetune a multi-view diffusion model that jointly generates multiple views of objects in a feedforward fashion, outperforming image-based methods at a fraction of their compute budget. We release code and model weights at https://github.com/Stability-AI/generative-models .
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