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Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion
Yangfan He, Sida Li, Jianhui Wang, Kun Li, Xinyuan Song, Xinhang Yuan, Keqin Li, Kuan Lu, Menghao Huo, Jingqun Tang, Yi Xin, Jiaqi Chen, Miao Zhang, Xueqian Wang
TL;DR
Frame-independent T2I generation and existing temporal strategies leave a trade-off between temporal consistency, quality, and training cost. GE-Adapter combines temporal-spatial and semantic consistency with Baliteral DDIM inversion through lightweight adapter modules. The paper reports improved perceptual quality, text-image alignment, and frame-to-frame coherence, while noting a flexibility limitation when unshared tokens are heavily weighted.
Problem
Existing approaches face limited temporal consistency, high training costs, lower-quality outputs, or restricted adaptability in T2V editing.
Method
GE-Adapter combines Baliteral DDIM inversion with FTC temporal blocks, SCD spatial blocks, and TSC shared and frame-specific token modules.
Results
Over 50% efficiency improvement is reported with a lightweight adapter, alongside enhanced temporal consistency, semantic alignment, video quality, and diverse-condition performance.
Takeaways & Limitations
The framework offers a plug-and-play approach for improving perceptual quality, text-image relevance, and temporal consistency across video editing systems.
Takeaways & Limitations
Heavily weighting unshared tokens preserves original-video attributes but can limit flexibility for novel textual prompts.
Abstract
from arXiv · showhide
Recent advancements in text-to-image (T2I) generation using diffusion models have enabled cost-effective video-editing applications by leveraging pre-trained models, eliminating the need for resource-intensive training. However, the frame-independence of T2I generation often results in poor temporal consistency. Existing methods address this issue through temporal layer fine-tuning or inference-based temporal propagation, but these approaches suffer from high training costs or limited temporal coherence. To address these challenges, we propose a General and Efficient Adapter (GE-Adapter) that integrates temporal-spatial and semantic consistency with Baliteral DDIM inversion. This framework introduces three key components: (1) Frame-based Temporal Consistency Blocks (FTC Blocks) to capture frame-specific features and enforce smooth inter-frame transitions via temporally-aware loss functions; (2) Channel-dependent Spatial Consistency Blocks (SCD Blocks) employing bilateral filters to enhance spatial coherence by reducing noise and artifacts; and (3) Token-based Semantic Consistency Module (TSC Module) to maintain semantic alignment using shared prompt tokens and frame-specific tokens. Our method significantly improves perceptual quality, text-image alignment, and temporal coherence, as demonstrated on the MSR-VTT dataset. Additionally, it achieves enhanced fidelity and frame-to-frame coherence, offering a practical solution for T2V editing.
1 INTRODUCTION
Existing T2V editing methods trade off computational efficiency, adaptability, output quality, and temporal consistency. The proposed lightweight consistency-adapter framework combines temporal, spatial, and semantic consistency to reduce these trade-offs.
- T2V generation and editing aim to reflect textual descriptions while maintaining visual coherence, spatial consistency, and temporal continuity.
- Pretrained T2I diffusion models provide lower-cost alternatives for video editing, including training-based and training-free strategies.
- Training-based methods improve temporal consistency through layer fine-tuning but incur high training costs and limited scalability.
- Training-free feature propagation eliminates training costs but can produce lower-quality outputs.
- The proposed framework combines FTC temporal blocks, SCD spatial blocks, and TSC token modules to improve consistency while minimizing training expenses.FTC Blocks reduce abrupt feature changes, SCD Blocks reduce noise and artifacts, and TSC uses shared and frame-specific tokens.
- The lightweight adapter uses 0.755M UNet parameters and 15.4M prompt parameters, with a total size of 860M, and reports over 50% efficiency improvement.The framework is also reported to enhance temporal consistency, semantic alignment, and video quality.
2 RELATED WORK
Prior T2V approaches improve video generation or frame quality but often rely on costly training and complex temporal mechanisms. The proposed framework is presented within a broader line of inversion-, adapter-, and consistency-based approaches.
- T2V methods include inversion and sampling approaches, upstream adapter-based fine-tuning, and downstream methods using complex attention mechanisms.
- Long-video methods target temporal coherence, while other approaches focus on improving the quality of individual video frames.
- Existing 2D U-Net-based T2V models may require training from scratch, freezing pretrained T2I models and relying on complex temporal layers.
- These approaches lack a unified, efficient, lightweight training paradigm with strong generalization from a single run and plug-and-play usability.
- DDIM inversion controls latent-space content without regenerating the entire image, enabling adjustments to object shapes, styles, and details while maintaining consistency.
3 METHOD
The method uses a lightweight adapter framework to jointly improve temporal, spatial, and semantic consistency in low-cost T2V editing. It combines multi-frame latent processing, temporal and spatial coherence blocks, token-based conditioning, and bilateral-filtered inversion.
- 3 METHOD: The framework targets temporal, spatial, and semantic consistency while reducing training costs in 2D UNet-based T2V generation.It is designed as a lightweight video adapter for efficient video editing.
- 3.1 Base Diffusion model: Multi-frame latent processing encodes consecutive frames, injects frame-specific noise, and concatenates latent representations for joint spatial-temporal modeling.Independent noise preserves frame-specific lighting, texture, and motion while concatenated latents support cross-frame modeling.
- 3.2 Hierarchical-Aware Temporal-Spatial Consistency Module: FTC Blocks align adjacent feature maps with a temporal consistency loss to reduce flickering and produce smoother frame transitions.Lightweight trainable adapters extract intermediate UNet features, while the loss encourages alignment across consecutive frames.
- 3.2 Hierarchical-Aware Temporal-Spatial Consistency Module: SCD Blocks apply bilateral filtering during DDIM inversion to smooth noisy latents, preserve edges, and improve frame-to-frame spatial consistency without additional training.The filter uses spatial proximity and intensity similarity to reduce artifacts while retaining structure.
- 3.3 Token-Based Semantic Consistency Module: The TSC Module combines shared tokens for global semantic consistency with frame-specific tokens for local details and per-frame variation.The final embedding concatenates shared, frame-specific, and conditional/unconditional embeddings to guide frame-aware denoising.
4 EXPERIMENTS
Experiments on MSR-VTT evaluate the adapter’s training setup, dataset protocol, and compatibility across algorithms and conditioning modes. Results show consistent gains in perceptual quality, semantic alignment, and temporal coherence.
- Implementation: The model uses Stable Diffusion v1.5 with ControlNet and a jointly trained PrefixToken module, trained for 20 hours on four RTX4090 GPUs.Training used mixed precision, a 3e-5 learning rate, and 512×512 input frames.
- Dataset: MSR-VTT contains 10,000 clips across 20 categories, split into 6,513 training, 497 validation, and 2,990 test clips.Captions were augmented, adjacent frames were batched during early denoising, and videos were converted to WebDataset format.
- Main Results: Under diverse ControlNet settings, the adapter improves FID and LPIPS while increasing CLIP, with gains reported for Canny Edge, Human Pose, and Depth Map conditioning.For Human Pose conditioning, CLIP increases from 29.49 to 33.56, FID improves from 365.91 to 362.83, and LPIPS from 0.763 to 0.746.
- Main Results: Across T2I-based T2V algorithms, the adapter improves perceptual quality and text-image alignment, including LPIPS reductions and CLIP gains for Text2Video-Zero, TokenFlow, Vid2Vid, and VidToMe.Examples include Text2Video-Zero LPIPS changing from 0.402 to 0.319 and CLIP from 28.82 to 31.38, while TokenFlow’s FID changes from 166.72 to 143.21.
5 ABLATION STUDY
The ablations show that semantic-token design, adapter timing, bilateral filtering, and inversion settings jointly affect video quality, coherence, and text alignment. The experiments identify trade-offs between preserving original-video attributes, prompt flexibility, detail sharpness, and smoothness.
- Token-Based Semantic Consistency: Shared tokens preserve global semantic alignment, while removing them causes TokenFlow’s CLIP score to drop from 29.4 to 20.1 and FID to increase from 135.7 to 387.8.LPIPS also rises from 0.14 to 0.17, with degraded global coherence and local detail quality.
- Token-Based Semantic Consistency: Increasing unshared-token strength preserves original-video attributes but can limit flexibility for novel textual prompts.The limitation is illustrated by TokenFlow on “A lion is walking on the grass” and Text2Video on “A man is running.”
- Token-Based Semantic Consistency: A suitable shared/unshared-token balance is necessary, with 18 shared tokens reported as an effective Prompt Adapter setting.Too many unshared tokens degrade global semantics and frame transitions, while excessive shared tokens risk reducing text specificity.
- Adapter Activation: A 0.5-1.0 training range with the same inference range achieves the best metrics, but its excessive constraints can over-smooth details and cause blur.The study therefore adopts a narrower 0.9-1.0 inference range while retaining the broader training range.
- Hierarchical-Aware Temporal-Spatial Consistency: Bilateral filtering improves TokenFlow’s FID from 135.66 to 122.52, CLIP from 29.17 to 29.35, and LPIPS from 0.142 to 0.136.The filtering reduces blur and jitter in generated frames and improves perceptual quality and coherence.
- Bilateral Filtering DDIM Inversion: Smaller inversion step sizes improve temporal coherence and produce smoother transitions, while increasing kernel size and corresponding steps can improve PSNR and LPIPS.These settings are associated with reduced noise, fewer flickering or blurry transitions, and clearer outputs.
6 CONCLUSION
The paper introduces GE-Adapter, a prompt-learning, plug-and-play adapter for text-guided video editing with pre-trained text-to-image diffusion models. It combines temporal, spatial, and semantic consistency with Baliteral DDIM inversion to improve video quality and alignment at minimal training cost.
- Conclusion: GE-Adapter improves temporal consistency and visual quality in text-guided video editing using pre-trained text-to-image diffusion models.The adapter is described as prompt-learning and plug-and-play.
- Conclusion: The framework combines FTC, SCD, and TSC components to improve temporal coherence, spatial consistency, perceptual quality, and text-image relevance.FTC captures frame-specific information, SCD reduces noise and artifacts, and TSC uses shared and frame-specific tokens.
A QUALITATIVE RESULTS
Qualitative comparisons show that the adapter improves temporal stability, semantic alignment, and visual coherence across video-editing scenarios and algorithms. Attention and token visualizations further examine how adapter use and token configuration affect generated videos.
- Visual Comparisons: Across lion roaring, biking through water, walking dog, and surfing examples, the adapter produces smoother motion, fewer artifacts, and stronger semantic alignment.Reported improvements include coherent facial features, realistic water effects, natural strides, and preserved surfer-wave interaction.
- Attention Visualization: Cross-attention comparisons between base and adapter-enhanced Stable Diffusion models show minor attention-map differences during image generation.The comparison evaluates attention maps across the two model variants.
- Ablation Visualizations: The qualitative studies compare video-generation algorithms with and without adapters under different token strategies, shared-token counts, and training or inference ranges.The figures include shared, enhanced-unshared, and removed-shared token configurations, plus temporal-aware loss and adapter activation ranges.