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SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-driven Video Editing

Nazmul Karim, Umar Khalid, Mohsen Joneidi, Chen Chen, Nazanin Rahnavard

arXiv:2305.18670v2cs.CV

TL;DR

Adapting text-to-image diffusion models for video is less costly than training text-to-video models on large datasets, but per-video adaptation remains time-consuming. SAVE tunes spectral shifts and regularizes larger singular values more tightly than smaller ones. Its evaluations cover multiple editing scenarios, and adaptation is reported to be almost 10× faster.

  • Problem

    Adapting text-to-image models for video is less costly than training text-to-video models on large datasets, but per-video adaptation still takes significant time.

    Source: §1 · p. 1

  • Method

    SAVE adapts pretrained text-to-image weights by tuning singular values while freezing singular vectors, with a regularizer that constrains larger singular values more tightly.

    Source: §1 · p. 2, §3.2 · p. 4, §3.2.1 · p. 6

  • Results

    SAVE adaptation is almost 10× faster, and its edits align with text descriptions while preserving fidelity to the original videos.

    Source: §1 · p. 2, §4.2 · p. 7

  • Takeaways & Limitations

    SAVE offers a lower-parameter, more computationally efficient approach to text-guided video editing across scenarios including style transfer and object replacement.

    Source: §4.2 · p. 6, §5 · pp. 8–9

    LoRA may offer better flexibility than SAVE for extensive fine-tuning across multiple concepts or videos.

    Source: §4.3 · p. 8

Abstract

from arXiv · show

Text-to-Image (T2I) diffusion models have achieved remarkable success in synthesizing high-quality images conditioned on text prompts. Recent methods have tried to replicate the success by either training text-to-video (T2V) models on a very large number of text-video pairs or adapting T2I models on text-video pairs independently. Although the latter is computationally less expensive, it still takes a significant amount of time for per-video adaption. To address this issue, we propose SAVE, a novel spectral-shift-aware adaptation framework, in which we fine-tune the spectral shift of the parameter space instead of the parameters themselves. Specifically, we take the spectral decomposition of the pre-trained T2I weights and only update the singular values while freezing the corresponding singular vectors. In addition, we introduce a spectral shift regularizer aimed at placing tighter constraints on larger singular values compared to smaller ones. This form of regularization enables the model to grasp finer details within the video that align with the provided textual descriptions. We also offer theoretical justification for our proposed regularization technique. Since we are only dealing with spectral shifts, the proposed method reduces the adaptation time significantly (approx. 10 times) and has fewer resource constraints for training. Such attributes posit SAVE to be more suitable for real-world applications, e.g. editing undesirable content during video streaming. We validate the effectiveness of SAVE with an extensive experimental evaluation under different settings, e.g. style transfer, object replacement, privacy preservation, etc.

1. Introduction

Diffusion-based image editing is established, but adapting text-to-image models for video remains constrained by limited temporal awareness and the cost of tuning many parameters. SAVE addresses this with spectral-shift tuning and frame attention.

1. Introduction

  • Diffusion models support high-quality text-guided media synthesis, and personalized image diffusion methods use parameter fine-tuning for user-specific edits.The paper focuses on an efficient text-guided video-editing framework.Source: §1 · p. 1
  • Adapting image diffusion models is more feasible than training video models on large text-video datasets, but temporal awareness and parameter-tuning overhead remain challenges.The adapted model is trained on one video rather than millions, so preserving much of the image model's generalization capability matters for editability.Source: §1 · p. 1
  • SAVE adapts image diffusion models by updating singular values while freezing singular vectors, reducing trainable parameters and speeding adaptation almost 10×.Its spectral-shift regularizer constrains larger singular values more tightly than smaller ones to help learn video details matching text descriptions.Source: §1 · pp. 1–2, Figure 1 · p. 2
  • Frame attention is proposed to enforce spatial and temporal consistency across frames while offering better efficiency.The paper reports evaluating SAVE across different benchmarks with qualitative and quantitative results.Source: §1 · p. 2

2. Related Work

Related work spans text-to-image and text-to-video generation, as well as diffusion-based video editing. SAVE extends the use of pretrained image diffusion models toward efficient video editing.

2. Related Work

  • Text-to-video generation trails text-to-image generation amid limited large-scale text-video data and challenges in temporal consistency and coherence.Prior approaches include video-focused models and methods transferring advances from text-to-image generation.Source: §2 · pp. 2–3
  • SAVE expands latent diffusion models into the spatiotemporal domain and fine-tunes a pretrained text-to-image model on a single text-video pair.Its framework uses an inflated image model, frame attention, and spectral-value tuning.Source: §2 · p. 3, Figure 2 · p. 3
  • Video diffusion models can be computationally challenging and require large-scale captioned image and video training data.One-shot text-to-image-based editors such as Tune-A-Video inflate the model and fine-tune temporal attention weights.Source: §2 · p. 3
  • Diffusion-based image editing and video-editing frameworks provide the surrounding research context for SAVE.The cited video-editing approaches include Dreamix, Gen-1, and Tune-A-Video.Source: §2 · p. 3

3. Method

SAVE adapts a pretrained T2I model to edit a video from a single text-video pair by tuning spectral shifts. It combines regularized spectral adaptation with cross-frame attention for temporal modeling.

3. Method

  • SAVE uses a pretrained T2I model and a single text-video pair to generate a video edited according to a target prompt.The input video and its prompt are denoted X and P; the editing prompt is P∗.Source: §3 · p. 3

3.1. Preliminaries

  • Stable Diffusion operates in an autoencoder latent space, where an encoder compresses an image and a decoder recovers it.The passage identifies the encoder E and decoder D.Source: §3.1 · p. 3

3.2. Spectral-Shift-Aware Video Editing (SAVE)

  • SAVE inflates 2D convolutions into pseudo-3D layers and replaces self-attention with spatiotemporal attention, then fine-tunes query matrices while freezing convolution layers.It controls the spectral shifts of the query weights rather than fine-tuning the weights directly.Source: §3.2 · p. 4

3.2.1 Regularized Spectral Shift

  • A linear spectral generator samples spectral coefficients from per-component normal distributions; Theorem 1 bounds the difference between an original singular value and the expected generated singular value.The bound is defined using the singular values of the data matrix and generated matrix.Source: §3.2.1 · p. 5
  • The spectral-shift regularizer constrains changes to larger singular values more tightly while allowing smaller singular values to vary more for fine details.The passage also describes the regularizer as controlling the nature of adaptation, while the loss’s first term adapts the model to new data.Source: §3.2 · p. 4, §3.2.1 · p. 6

3.2.2 Temporal Modeling

  • Frame attention is more memory-efficient and faster than sparse-causal attention, which falters in rapid-motion editing; SAVE combines frame attention with cross-attention and temporal attention.The framework fine-tunes query projections for frame and cross-attention to support semantically consistent image sets.Source: §3.2.2 · p. 6

4. Evaluation

SAVE is evaluated for video editing across implementation settings, editing tasks, and baseline comparisons. The reported examples cover style transfer, object and attribute editing, runtime efficiency, and the effects of regularization and attention choices.

4.1. Implementation Details

  • The implementation uses pretrained Stable Diffusion weights and takes approximately 3 minutes to fine-tune each video, followed by approximately 20 seconds of sampling on an NVIDIA 3090 GPU.The described setup uses uniformly spaced 512 × 512 frames, 200 epochs, a learning rate of 1e−3, and a batch size of 1.Source: §4.1 · p. 6

4.2. Experimental Results

  • SAVE transfers video style while preserving semantic content, edits objects in line with text while retaining fidelity, and supports editing personal attributes while maintaining appearance, action, and realism.The evaluation also describes multi-attribute editing with a larger regularizer coefficient, lower learning rate, and longer training.Source: §4.2 · pp. 6–7
  • For a 256 × (128 × 5 × 5) convolution weight matrix, SAVE optimizes 256 singular values instead of 8,19,200 parameters, a 3200× reduction, and reduces editing time.The reported per-video fine-tuning takes approximately 3 minutes, with sampling taking approximately 20 seconds on an NVIDIA 3090 GPU.Source: §4.1 · p. 6, §4.2 · p. 7

4.3. Comparison with Baselines

  • Against baselines, SAVE supports localized edits while preserving input structure and temporal coherence; the reported examples show Text2LIVE struggling with a black-to-white swan edit and a Jeep-to-Porsche replacement.Tune-A-Video is described as altering the entire video when selectively editing an object.Source: §4.3 · pp. 7–8, Figure 8 · p. 8
  • Without the proposed regularizer, fine-matching text prompts to video content is weaker, with limited editability and unwanted influence on other regions.The authors describe the regularizer as crucial to their reported state-of-the-art performance.Source: §4.3 · p. 8, Figure 8 · p. 8
  • SAVE modifies pretrained weights without adding weight layers and has O(min(h,w)) storage and update requirements, while rank-1 LoRA has O(h+w) requirements.The authors note that LoRA may offer more flexibility for extensive fine-tuning, such as few-shot adaptation or editing multiple videos.Source: §4.3 · p. 8
  • Replacing dense spatial-temporal attention with sparse-causal attention distorts the running horse, with overemphasis on previous frames linked to error propagation and later artifacts.The authors report comparable performance between frame attention with temporal attention and spatio-temporal attention across their experiments.Source: §4.3 · p. 8, Figure 8 · p. 8

5. Conclusion

The conclusion presents SAVE as a text-guided video-editing approach that adapts an image diffusion model by tuning spectral shifts. Its evaluations span varied scenarios, and the authors provide theoretical justification for the regularizer.

5. Conclusion

  • SAVE adapts an image-based diffusion model for text-guided video editing by fine-tuning spectral shifts rather than the full parameter set.The conclusion says this reduces the number of adjustable parameters and improves computational efficiency.Source: §5 · p. 8
  • The spectral-shift regularizer constrains larger singular values more than smaller ones, helping the model capture nuanced video details aligned with text.The authors also offer theoretical justification for the regularization technique.Source: §5 · pp. 8–9
  • The authors report comprehensive evaluations of SAVE across various scenarios.Source: §5 · p. 9

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