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AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, Bo Dai
TL;DR
Personalized text-to-image models are difficult to animate while preserving their visual quality and domain knowledge. AnimateDiff adds a reusable motion module, with MotionLoRA for adapting motion patterns, and evaluations show coherent animations across diverse personalized models.
Problem
Adding motion dynamics to personalized text-to-image models while preserving visual quality and domain knowledge remains an open challenge.
Method
AnimateDiff trains a plug-and-play motion module for integration with personalized models, while MotionLoRA adapts it to new motion patterns through lightweight fine-tuning.
Results
Extensive evaluations across diverse personalized text-to-image models show natural, coherent animations that remain faithful to personalized domains, with compatibility for controllable generation.
Takeaways & Limitations
AnimateDiff provides a practical way to animate community personalized models and support controllable animation without additional training.
Takeaways & Limitations
The method may be misused to generate harmful or misleading human-related content, including through inappropriate personalized models created by other artists.
Abstract
from arXiv · showhide
With the advance of text-to-image (T2I) diffusion models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. However, adding motion dynamics to existing high-quality personalized T2Is and enabling them to generate animations remains an open challenge. In this paper, we present AnimateDiff, a practical framework for animating personalized T2I models without requiring model-specific tuning. At the core of our framework is a plug-and-play motion module that can be trained once and seamlessly integrated into any personalized T2Is originating from the same base T2I. Through our proposed training strategy, the motion module effectively learns transferable motion priors from real-world videos. Once trained, the motion module can be inserted into a personalized T2I model to form a personalized animation generator. We further propose MotionLoRA, a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost. We evaluate AnimateDiff and MotionLoRA on several public representative personalized T2I models collected from the community. The results demonstrate that our approaches help these models generate temporally smooth animation clips while preserving the visual quality and motion diversity. Codes and pre-trained weights are available at https://github.com/guoyww/AnimateDiff.
1 INTRODUCTION
AnimateDiff addresses animation of personalized text-to-image models with a plug-and-play motion module trained on video motion priors and integrated without model-specific fine-tuning. The paper also introduces MotionLoRA for adapting motion patterns and evaluates the approach across diverse personalized models, including controllable animation with ControlNet.
- AnimateDiff: AnimateDiff trains a plug-and-play motion module on video datasets to learn motion priors, then integrates it into personalized T2Is for smooth animations.The described training data includes WebVid-10M.
- AnimateDiff: AnimateDiff enables animation generation for personalized T2Is without specific fine-tuning.The framework is designed to preserve personalized models’ visual quality and domain knowledge.
- Evaluation: Experiments evaluate AnimateDiff and MotionLoRA on community personalized T2I models spanning domains from 2D cartoons to realistic photographs.The models were collected from model-sharing platforms and form a diverse evaluation benchmark.
- Controllable animation: The motion module integrates with content-controlling approaches such as ControlNet without additional training for controllable animation generation.This extends AnimateDiff to controllable animation settings.
- MotionLoRA: MotionLoRA is a lightweight fine-tuning technique that adapts pre-trained motion modules to new motion patterns.This is presented as a separate contribution alongside AnimateDiff.
2 RELATED WORK
Related work spans text-to-image diffusion models, efficient personalization of pretrained T2Is, and methods for animating personalized T2Is. Existing animation approaches include cinematography generation and extending pretrained T2Is with temporal structures.
- Text-to-image diffusion models: Text-to-image diffusion models have attracted significant attention in academic and non-academic communities.Representative foundations and systems include GLIDE and classifier guidance.
- Personalizing T2I models: Personalization methods introduce concepts or styles into base T2I models using reference images.Complete fine-tuning can improve overall quality but may cause catastrophic forgetting.
- Animating personalized T2Is: Few works address animating personalized T2Is, including Text2Cinemagraph, which generates cinematography through flow prediction.Video-generation methods commonly extend pretrained T2Is with temporal structures.
3 PRELIMINARY
The paper builds on Stable Diffusion as its base text-to-image model and uses LoRA for efficient parameter adaptation. Stable Diffusion performs latent-space diffusion with a text-conditioned UNet, while LoRA trains low-rank updates instead of the full model.
- Stable Diffusion: Stable Diffusion is selected as the base T2I model because it is open-source and supported by a community producing many high-quality personalized models.Its diffusion process operates in the latent space of a pretrained autoencoder.
- Stable Diffusion: Stable Diffusion encodes images into latent variables, adds timestep-dependent Gaussian noise, and trains a denoising network to predict that noise using MSE loss.The noise strength is determined by predefined ᾱ_t values across diffusion steps t = 1, ..., T.
- Stable Diffusion: The denoising network is a UNet with four-resolution down/up blocks and a middle block containing ResNet, spatial self-attention, and text-conditioned cross-attention layers.The text encoder maps each prompt to a vector sequence used by the cross-attention layers.
- Low-rank adaptation (LoRA): LoRA fine-tunes large models by freezing original weights and optimizing newly added rank-decomposition matrices, reducing trainable parameters and catastrophic forgetting risk.It is applied primarily to attention layers, further reducing fine-tuning cost and storage.
4 ANIMATEDIFF
AnimateDiff learns transferable motion priors with a train-once motion module that plugs into personalized T2I models from the same base model. Its domain adapter mitigates video–image quality gaps during training, while optional MotionLoRA adapts motion to new patterns efficiently.
- Framework: A motion module and optional MotionLoRA can be inserted directly into a personalized T2I to generate animations through iterative denoising.The motion module learns transferable motion priors from video data, while MotionLoRA supports additional motion adaptation.
- Training stages: AnimateDiff trains a domain adapter, motion module, and optional MotionLoRA for domain-gap mitigation, motion-prior learning, and motion-pattern adaptation, respectively.The domain adapter is used only during training; the other modules form the animation generator.
- Domain adapter: The domain adapter isolates video-domain information in LoRA layers and is removed at inference to preserve the base T2I’s knowledge and reduce quality-gap effects.It is optimized on static video frames, with adapter strength α adjustable at inference and α = 0 removing its effects.
- Motion module: The motion module inflates the image diffusion model for 5D video tensors and uses a temporal Transformer to exchange information across frames.Self-attention lets each frame incorporate information from other frames, enabling the model to capture visual changes over time.
- MotionLoRA: MotionLoRA adapts a pretrained motion module to patterns such as zooming, panning, and rolling using as few as 20 ∼50 reference videos and 2,000 training iterations.The reported setup takes around 1 ∼2 hours and uses about 30M storage space, with reference videos obtained through rule-based augmentation.
5 EXPERIMENTS
Experiments evaluate AnimateDiff on diverse community-personalized T2I models against adapted video-generation methods and commercial tools, using user studies and CLIP metrics. Ablations examine the domain adapter, motion-module design, MotionLoRA efficiency, and compatibility with ControlNet-based depth control.
- Evaluation: AnimateDiff is evaluated on diverse personalized T2Is collected from Civitai, with prompts constructed from model homepages to activate domain-specific trigger words.The benchmark spans a wide range of domains, and eight qualitative results are shown.
- Baselines: Comparisons include Text2Video-Zero, Tune-a-Video, Gen-2, and Pika Labs for animating personalized T2Is.The first two are adaptable video-generation methods, while Gen-2 and Pika Labs are commercial tools.
- Quantitative evaluation: Quantitative evaluation measures text alignment, domain similarity, and motion smoothness using user studies and CLIP metrics.Average User Ranking uses higher scores to indicate stronger preference, while domain-similarity CLIP compares animation frames with personalized-T2I reference images.
- Ablations: Reducing the domain-adapter scaler improves overall visual quality while reducing visual-content distribution learned from the video dataset.The study varies the scaler from 1, representing full impact, to 0, representing complete removal.
- Ablations: MotionLoRA learns new camera motions with a small parameter scale and succeeds with N = 50 reference videos while maintaining comparable motion quality.The experiments assess both parameter efficiency and data efficiency; zoom-in is given as an example camera motion.
- Controllable generation: AnimateDiff’s separated learning of visual content and motion priors enables integration with ControlNet for depth-map-sequence-controlled generation.This demonstrates direct application of existing content-control approaches to controllable generation.
6 CONCLUSION
AnimateDiff is a practical pipeline that turns personalized text-to-image models into animation generators without compromising quality or losing pre-learned domain knowledge. Its motion module and MotionLoRA enable coherent, domain-faithful motion and lightweight motion personalization.
- AnimateDiff directly converts personalized text-to-image models into animation generators without compromising quality or losing pre-learned domain knowledge.
- Three component modules learn meaningful motion priors, alleviate visual quality degradation, and enable motion personalization through the lightweight MotionLoRA technique.
- Once trained, the motion module integrates into other personalized text-to-image models to generate natural, coherent animations faithful to the personalized domain.
7 ETHICS STATEMENT
The authors condemn harmful or misleading uses of generative AI while acknowledging that AnimateDiff can be misused for human-related content and through inappropriate personalized models. They emphasize legal, privacy, and positive-content standards, and suggest an additional safety checker as a potential mitigation.
- The authors strongly condemn using generative AI to harm individuals or spread misinformation.
- AnimateDiff may be misused because it generates human-related content and incorporates personalized models developed by other artists that may contain inappropriate content.
- The authors uphold legal compliance, privacy rights, and positive-content generation, while proposing an additional Stable Diffusion-like safety checker as a potential solution.