Ensuring Reproducibility in AI Research: Code and Pre-trained Weights Open-Sourced
19 Nov 2024
This statement outlines efforts to ensure the reproducibility of AI research
AnimateDiff Ethics Statement: Ensuring Responsible Use of Generative AI for Animation
19 Nov 2024
AnimateDiff acknowledges the potential misuse of generative AI for harmful content and is committed to upholding ethical standards.
How AnimateDiff Transforms T2I Models into High-Quality Animation Generators with MotionLORA
19 Nov 2024
AnimateDiff transforms personalized T2I models into high-quality animations, using MotionLoRA for motion personalization and seamless animation generation.
AnimateDiff Combines with ControlNet for Precise Motion Control and High-Quality Video Generation
19 Nov 2024
AnimateDiff's ability to separate visual content and motion priors allows for precise control over video generation.
Ablative Study on Domain Adapter, Motion Module Design, and MotionLoRA Efficiency
19 Nov 2024
Ablative study of AnimateDiff reveals how domain adapter, motion module design, and MotionLoRA improve visual quality, motion learning, and efficiency.
User Preferences and CLIP Metrics: Results of AnimateDiff’s Performance in Video Generation
18 Nov 2024
Explore AnimateDiff’s performance with user rankings and CLIP metrics, comparing text alignment, domain similarity, and motion smoothness in video generation.
How AnimateDiff Brings Personalized T2Is to Life with Efficient Motion Modeling
18 Nov 2024
AnimateDiff enables efficient animation of personalized T2Is, outperforming other models with MotionLoRA for improved shot control and composition.
AnimateDiff in the Wild
18 Nov 2024
Explore the training and inference process of AnimateDiff, detailing how the domain adapter, motion module, and MotionLoRA work in tandem.
Mastering Motion Dynamics in Animation with Temporal Transformers
18 Nov 2024
The AnimateDiff motion module uses temporal Transformers to model motion dynamics, enabling smooth, high-quality animations from 2D diffusion models.