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Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer

Nisha Huang, Yuxin Zhang, Weiming Dong

arXiv:2305.05464v1cs.CVcs.MM

TL;DR

Video stylization with text-guided diffusion is challenging because text-video data and training resources are limited, while preserving video content through diffusion is difficult. Style-A-Video uses denoising guidance based on text, video frames, and attention maps, with sampling optimization and temporal consistency. The paper reports improved temporal and prompt consistency over baselines, comparable content preservation to TextLIVE, and higher visual quality and effectiveness overall.

  • Problem

    Text-to-video data and training resources are limited, and diffusion's destructive noise addition makes preserving input video content challenging.

    Source: §1 · pp. 1–2

  • Method

    Style-A-Video performs zero-shot text-driven video stylization by guiding denoising with text, video-frame content, and attention-map structure conditions, alongside temporal consistency.

    Source: §1 · p. 2, §3 · p. 3

  • Results

    Style-A-Video reports superior temporal and prompt consistency to baselines, comparable content preservation to TextLIVE, and higher visual quality and effectiveness among corresponding baselines.

    Source: §1 · p. 2, §4.3 · p. 7

  • Takeaways & Limitations

    The framework is reported to stylize arbitrary videos and styles with faster inference, without additional training or per-video fine-tuning.

    Source: §1 · p. 2, §5 · p. 8

Abstract

from arXiv · show

Large-scale text-to-video diffusion models have demonstrated an exceptional ability to synthesize diverse videos. However, due to the lack of extensive text-to-video datasets and the necessary computational resources for training, directly applying these models for video stylization remains difficult. Also, given that the noise addition process on the input content is random and destructive, fulfilling the style transfer task's content preservation criteria is challenging. This paper proposes a zero-shot video stylization method named Style-A-Video, which utilizes a generative pre-trained transformer with an image latent diffusion model to achieve a concise text-controlled video stylization. We improve the guidance condition in the denoising process, establishing a balance between artistic expression and structure preservation. Furthermore, to decrease inter-frame flicker and avoid the formation of additional artifacts, we employ a sampling optimization and a temporal consistency module. Extensive experiments show that we can attain superior content preservation and stylistic performance while incurring less consumption than previous solutions. Code will be available at https://github.com/haha-lisa/Style-A-Video.

1 INTRODUCTION

The introduction frames video stylization as difficult because text-video data and training resources are limited, existing per-video approaches are costly, and diffusion can damage content. Style-A-Video targets fast arbitrary-video stylization with guidance for style, content, and structure, plus temporal consistency.

1 INTRODUCTION

  • Text-video generation lags image generation because matching text-video data are hard to collect, while training video models from scratch is resource-intensive.The introduction therefore presents leveraging current text-image models as more practical.Source: §1 · p. 1
  • Existing video-editing approaches still incur substantial computation and time through neural-layer-atlas training or per-video fine-tuning.The paper seeks quick inference for arbitrary videos to avoid that expense.Source: §1 · p. 2
  • Diffusion noise can damage input content, and text prompts specify what to change without specifying what to retain.The authors connect these issues to content modifications that fail stylization requirements.Source: §1 · p. 2
  • Style-A-Video combines text for style, video frames for content, and attention maps for detail guidance, then adds temporal consistency to reduce flicker.The method adapts noise prediction with custom guidance combined with classifier-free guidance, without fine-tuning or additional training.Source: §1 · p. 2
  • Style-A-Video performs arbitrary text-driven video styling at inference time without per-video training or fine-tuning.Its guidance formulas jointly control style, content, and structure, while the method also targets temporal and content consistency.Source: §1 · p. 2
  • Experiments and user studies are reported to show higher visual quality and effectiveness than corresponding baselines.The introduction presents this as evidence for the method's overall performance.Source: §1 · p. 2

2 RELATED WORK

Related work spans image stylization and text-guided image and video generation or editing, but existing video methods face trade-offs in cost, content retention, or temporal artifacts. Style-A-Video distinguishes itself by using text and extracted structure to guide semantically consistent video stylization.

Image and video style transfer

  • Earlier image-style transfer includes neural style transfer based on CNN feature-map inner products, but its successive optimization is time-consuming.Arbitrary image-style methods also align second-order style and content statistics, with AdaIN normalizing content features using style-feature means and variances.Source: §2 · p. 2
  • Arbitrary image-style approaches include StyTr2's separate content and style transformer encoders and CAST's style-distribution comparisons and image-attribute representations.These works address style transfer across image domains.Source: §2 · p. 2

Video style transfer

  • Many image-style-transfer methods have been applied to video, but flow-based sequence methods can produce ghosting when optical flow is inaccurate and are costly on long or high-resolution videos.The paper motivates a zero-shot approach intended to combine temporal consistency with stylistic expression.Source: §2 · p. 2
  • Unlike reference-image methods that statistically transfer style, Style-A-Video uses text or image style and content under extracted-structure constraints to produce semantically consistent outputs.The paper contrasts this with matching feature statistics.Source: §2 · p. 2

Text-to-image synthesis and editing

  • Text-guided image editing ranges from domain-limited GAN methods to large-scale text-image models, while diffusion methods support image generation and inpainting.The cited progression includes DALL-E's joint image-text distribution, DALL-E 2's CLIP latent space and prior, and Stable Diffusion's latent-space generation.Source: §2 · p. 2

Text-to-video synthesis and editing

  • Video editing methods include text-layer editing and per-video diffusion fine-tuning, but costly per-video training constrains creative-tool use and some editing loses input content.Text2LIVE propagates edits across frames through a layered representation; Tune-A-Video optimizes an image-model video on a single video.Source: §2 · p. 3

3 METHODS

Style-A-Video stylizes input videos from text while retaining their content and structure. Its diffusion pipeline combines text, frame, and attention guidance with sampling optimization and temporal refinement.

3 METHODS

  • Style-A-Video conditions video stylization on text while preserving input content and structure, then optimizes sampling and temporal consistency.Content means appearance and semantic information; structure means shape and geometric features.Source: §3 · p. 3

3.1 Diffusion Models

  • Latent diffusion operates in an autoencoder’s latent space, where a denoising model predicts noise using image, text, and attention-map conditions.A decoder reconstructs the latent representation back into pixel space.Source: §3.1 · p. 4

3.2 Condition Representation

  • Text prompts specify the target style, input-frame channels guide content retention, and self-attention features provide detail guidance.The method concatenates the encoded input frame with the noisy latent at the first convolutional layer.Source: §3 · p. 3, §3.2 · p. 4

3.3 Condition Guidance

  • Three adjustable guidance scales control how strongly generated samples correspond to the input image, text prompt, and self-attention map.The condition-specific guidance is combined with classifier-free guidance.Source: §3.3 · pp. 5–6

3.4 Sampling Optimization

  • A CLIP-based self-similarity structure loss guides sampling to permit appearance changes while retaining spatial layout, shape, and perceived semantics.The method takes a gradient step with respect to the latent to optimize the denoising network together with the guidance condition.Source: §3.4 · p. 6

3.5 Temporal Consistency

  • Global consistency optimization can leave local flicker, so a lightweight flow-based refinement network uses consecutive frames and temporal consistency loss to address it.The local refinement network further refines the output frames.Source: §3.5 · p. 6

4 EXPERIMENTS

Experiments compare Style-A-Video with text-guided video-editing baselines through qualitative evaluation, metrics, user studies, and ablations. Results report stronger temporal and prompt consistency, content accuracy comparable to Text2LIVE, and favorable user ratings.

4.1 Implementation Details

  • The implementation uses GPT-3 and Stable Diffusion; generating a 512×256 frame takes 1 second and 3925 MiB on an NVIDIA GeForce RTX 3090.Experiments use 30 diffusion steps and a time step of 30.Source: §4.1 · p. 7

4.2 Qualitative Evaluation

  • Style-A-Video maintains input content and motion while stylizing, whereas Tune-A-Video and VideoCrafter alter content and Deforum lacks frame coherence.Text2LIVE [2] preserves content well but is less expressive in meeting text-editing requirements.Source: §4.2 · p. 7

4.3 Quantitative Evaluation

  • In Table 1, Style-A-Video achieves superior temporal consistency and prompt consistency compared with the baselines, while its content-preservation accuracy is comparable to that of TextLIVE [2].Quantitative evaluation randomly selected 25 videos generated by Style-A-Video and each SOTA method.Source: §4.3 · p. 7

4.4 User Study

  • The first user study compares Style-A-Video with four open-source methods using 20 content-style pairs and collects 1,400 participant votes.Participants choose their preferred result in each displayed comparison.Source: §4.4 · p. 8
  • 70 participants contributed 11,200 scores, and Style-A-Video received good results across all four user-study indicators.The indicators cover agreement with the text style, input content, temporal consistency, and overall effects.Source: §4.4 · p. 8

4.5 Ablation Study

  • Increasing input-video guidance strengthens content agreement but weakens style agreement, while stronger text guidance increases stylization and content deviation.The guidance-scale ablation examines frame accuracy and prompt consistency.Source: §4.5 · p. 8, Figure 7 · p. 8
  • Removing the self-attention map loses fine details, while removing temporal consistency produces inter-frame color and illumination differences and flicker.The ablation varies each design individually.Source: §4.5 · p. 8

5 CONCLUSIONS AND FUTURE WORK

Style-A-Video combines style, content-retention, temporal-consistency, and structural guidance in a latent-diffusion framework. The authors identify additional conditions and pose estimation as future directions for studying video stability.

5 CONCLUSIONS AND FUTURE WORK

  • Style-A-Video uses text, video, and cross-attention graphs as simultaneous conditions for style, content-retention, temporal-consistency, and structural guidance.The framework is based on a latent diffusion model.Source: §5 · p. 8
  • The authors describe the framework as capable of generating arbitrary videos in arbitrary styles with faster inference.Source: §5 · p. 8
  • Future work will study how additional conditions, including three parameters and pose estimation, affect video stability.Source: §5 · p. 8

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