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AnimateDiff-Lightning: Cross-Model Diffusion Distillation

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arxiv 2403.12706 v1 pith:IH6LEAAB submitted 2024-03-19 cs.CV cs.AI

classification cs.CVcs.AI
keywords animatediff-lightningdiffusionvideodistillationdistilledgenerationmodelachieve
verification ladder T0 review T1 audit T2 compute T3 formal
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We present AnimateDiff-Lightning for lightning-fast video generation. Our model uses progressive adversarial diffusion distillation to achieve new state-of-the-art in few-step video generation. We discuss our modifications to adapt it for the video modality. Furthermore, we propose to simultaneously distill the probability flow of multiple base diffusion models, resulting in a single distilled motion module with broader style compatibility. We are pleased to release our distilled AnimateDiff-Lightning model for the community's use.

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Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. OSVE: One Step Video Editing with One Step Diffusion Models

    cs.CV 2026-07 conditional novelty 6.0 of 10

    OSVE performs text-guided video editing with one-step diffusion models by training a single-pass inversion encoder and unifying frame latents for cross-frame attention, achieving quality comparable to multi-step metho...

  2. DriftWorld: Fast World Modeling through Drifting

    cs.RO 2026-07 conditional novelty 6.0 of 10

    An action-conditioned world model trained with drifting generates robot rollout videos in one forward pass, matching diffusion quality while running 2.8-478x faster per-table, and raises GPC-RANK Push-T IoU from 0.635...

  3. Video Deepfake Abuse: How Company Choices Predictably Shape Misuse Patterns

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    A few open-weight video models and distribution platforms dominate the creation and spread of NSFW AI video, making developer and platform choices the main intervention points for reducing non-consensual deepfake abuse.

  4. Distribution Matching Distillation Meets Reinforcement Learning

    cs.CV 2025-11 conditional novelty 6.0 of 10

    Combining DMD distillation with RL during training produces few-step text-to-image models that outperform their multi-step teacher on several benchmarks.

  5. Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis

    cs.CV 2025-07 conditional novelty 6.0 of 10

    EVS combines a text-to-image and a text-to-video diffusion model in a single denoising pass, improving frame quality and temporal consistency without retraining.

  6. When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators

    cs.CV 2025-06 conditional novelty 6.0 of 10

    MotionEcho adaptively re-injects teacher-model guidance into few-step distilled video generators so reference motion can be copied at test time without training.

  7. Multi-Dimensional Quality Assessment for AI-Generated Human-Centric Videos: Dataset and Model

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A new large benchmark for AI-generated human-centric video quality with pairwise preferences, plus a Mixture-of-Experts MLLM that outperforms prior methods on rating, comparison, and Q&A.

  8. Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion

    cs.CV 2025-08 unverdicted novelty 4.0 of 10

    The submitted manuscript's abstract and full text are mismatched; the claimed 3D detection method is not present in the body.

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