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RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models
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Proposes RAVE, a training-free zero-shot video editing method that arranges frames into grids and randomly shuffles frame-to-grid assignments at each DDIM step so that a pre-trained text-to-image diffusion model (with ControlNet) yields temporally consistent edits faster and with lower memory than prior methods.
Problem
Text-guided video editing with pre-trained text-to-image diffusion models struggles with temporal consistency, long videos, shape edits, speed, and GPU memory; existing zero-shot methods rely on sparse-causal attention, optical flow, or feature smoothing.
Approach
Edit video as grids of frames processed by a T2I model (grid trick), and randomly reshuffle frames across grids at every denoising step so that convolution and self-attention layers propagate style globally across all frames without extra memory proportional to video length.
Key results
- RAVE achieves the highest CLIP-F (95.99 x10^-2) and CLIP-T (29.76 x10^-2) on 90-frame videos and the highest Qedit across all lengths (e.g., 23.95 x10^-5 on 90 frames).
- RAVE edits 90-frame videos in 4:28 total runtime versus 5:24 for TokenFlow and 5:33 for Text2Video-Zero, roughly 1 minute faster.
- In a user study with 130 participants, RAVE was chosen in the top two for general editing 90.51% of the time, temporal consistency 82.82%, and textual alignment 86.67%.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add noise shuffling at each denoising step (ablation) | RAVE · 8-, 36-, and 90-frame videos | CLIP-F (×10−2): 93.98 -> 95.95; 89.90 -> 93.18; 92.49 -> 95.99 | Helped |
| Add noise shuffling at each denoising step (ablation) | RAVE · 8-, 36-, and 90-frame videos | WarpSSIM (×10−2): 71.78 -> 71.44; 47.26 -> 48.81; 76.58 -> 80.51 | Mixed |
| Add noise shuffling at each denoising step (ablation) | RAVE · 8-, 36-, and 90-frame videos | CLIP-T (×10−2): 28.78 -> 29.51; 29.49 -> 29.93; 29.71 -> 29.76 | Mixed |
Surprising findings
- TokenFlow obtains higher WarpSSIM than RAVE on 8-frame videos (74.03 vs 71.44), so RAVE is not best on structural consistency for short clips.
Limitations
- Occasional flickering under extreme shape edits or local attribute edits, attributed to DDIM inversion approximation errors and LDM compression.
- Quantitative metrics (CLIP-F, WarpSSIM, CLIP-T) are coarse and can be high for unedited videos, so a combined Qedit metric is used.
- Frame count must be divisible by grid size; grid size limited by GPU memory.
Experimental setup
Datasets: custom video evaluation dataset (10 videos of 8 frames, 15 of 36 frames, 6 of 90 frames)
Benchmarks: RAVE video evaluation dataset
Metrics: CLIP-F WarpSSIM CLIP-T Qedit user study preference runtime
Baselines: Text2Video-Zero Rerender TokenFlow Pix2Video FateZero RAVE w/o shuffle
Builds on: Stable Diffusion DDIM ControlNet Tune-A-Video Pix2Video
Closest papers by meaning
- FADE: Frequency-Aware Diffusion Model Factorization for Video Editing (CVPR 2025 · similarity 0.89)
- FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling (ICLR 2024 · similarity 0.86)
- COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video Editing (NeurIPS 2024 · similarity 0.86)
- MeDM: Mediating Image Diffusion Models for Video-to-Video Translation with Temporal Correspondence Guidance (AAAI 2024 · similarity 0.86)
- Hierarchical Patch Diffusion Models for High-Resolution Video Generation (CVPR 2024 · similarity 0.86)
- Pathways on the Image Manifold: Image Editing via Video Generation (CVPR 2025 · similarity 0.86)
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