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AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
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Proposes AnimateDiff, a plug-and-play temporal Transformer motion module trained once on WebVid-10M to animate any personalized Stable Diffusion V1.5 model without model-specific tuning, plus MotionLoRA for adapting to new camera motions.
Problem
Animating existing personalized text-to-image diffusion models (e.g., DreamBooth/LoRA fine-tuned SD models) into temporally smooth animations without costly per-model tuning while preserving their visual quality and domain knowledge.
Approach
A motion module (temporal Transformer with sinusoidal position encoding, zero-initialized residual) is trained once on video data over a frozen, domain-adapted SD backbone, so it can be inserted into any personalized T2I sharing the same base; a separate domain adapter (LoRA) absorbs the video-data quality gap and is dropped at inference, and MotionLoRA adapts the motion module to new camera motio
Key results
- On the CLIP metric, AnimateDiff reaches 87.29 domain similarity and 98.00 smoothness, versus 84.84/96.57 for Text2Video-Zero and 80.68/97.42 for Tune-a-Video.
- In the user study, AnimateDiff has the top average ranking for smoothness (2.825) and text alignment (2.210), but ranks second on domain similarity (2.280 vs 2.620 for Text2Video-Zero).
- MotionLoRA can adapt to new motion patterns with as few as 20-50 reference videos, about 2,000 iterations (1-2 hours), and roughly 30M of extra storage.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the AnimateDiff motion module | Personalized T2I animation · Personalized T2I animation | User ranking, motion smoothness: 1.560; 1.615 -> 2.825 | Helped |
| Add the AnimateDiff motion module | Personalized T2I animation · Personalized T2I animation | CLIP domain similarity: 84.84; 80.68 -> 87.29 | Helped |
| Add the AnimateDiff motion module | Personalized T2I animation · Personalized T2I animation | CLIP motion smoothness: 96.57; 97.42 -> 98.00 | Helped |
| Add the AnimateDiff motion module | Personalized T2I animation · Personalized T2I animation | CLIP text alignment: 32.04; 35.98 -> 31.39 | Hurt |
| Add the AnimateDiff motion module | Personalized T2I animation · Personalized T2I animation | User ranking, text alignment: 1.620; 2.180 -> 2.210 | Helped |
| Add the AnimateDiff motion module | Personalized T2I animation · Personalized T2I animation | User ranking, domain similarity: 2.620; 1.100; 2.280 | Mixed |
| Use a temporal Transformer instead of 1D temporal convolution (ablation) | AnimateDiff motion module · Video motion modeling | Motion incorporation | Helped |
| Add a domain adapter during training (ablation) | AnimateDiff · Generated animation frames | Visual quality | Helped |
| Reduce the domain-adapter scaler from 1 toward 0 (ablation) | AnimateDiff · Generated animation frames | Visual quality | Helped |
| Reduce the domain-adapter scaler from 1 toward 0 (ablation) | AnimateDiff · Generated animation frames | Video-dataset visual content distribution | Hurt |
| Train MotionLoRA with 50 rather than 5 reference videos (ablation) | MotionLoRA · New motion patterns | Motion quality: N=5; N=50 | Helped |
Surprising findings
- Tune-a-Video achieves the highest text-alignment CLIP score (35.98) while AnimateDiff scores lower (31.39), and Text2Video-Zero beats AnimateDiff on user-rated domain similarity.
Limitations
- Text-alignment CLIP score is lower than Tune-a-Video's, and domain similarity in the user study trails Text2Video-Zero.
- Only Stable Diffusion V1.5-based personalized models are supported, since the motion module must share the same base T2I.
- Reduced data efficiency: with only 5 reference videos MotionLoRA degrades and learns texture rather than motion.
Experimental setup
Datasets: WebVid-10M LAION-Aesthetic
Benchmarks: user study on community personalized T2I models CLIP metric on text alignment, domain similarity, motion smoothness
Metrics: Average User Ranking CLIP score
Baselines: Text2Video-Zero Tune-a-Video
Builds on: Stable Diffusion LoRA DreamBooth ControlNet
Closest papers by meaning
- PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models (CVPR 2024 · similarity 0.89)
- MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model (CVPR 2024 · similarity 0.88)
- Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation (CVPR 2024 · similarity 0.88)
- VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models (AAAI 2025 · similarity 0.88)
- Enhanced Fine-Grained Motion Diffusion for Text-Driven Human Motion Synthesis (AAAI 2024 · similarity 0.87)
- Single Motion Diffusion (ICLR 2024 · similarity 0.87)
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