AV-DiT: Taming Image Diffusion Transformers for Efficient Joint Audio and Video Generation
Bibliographic record
Abstract
Recent Diffusion Transformers (DiTs) have shown impressive capabilities in generating single-modality content, including images, videos, and audio. However, the potential of DiTs to enable superb multimodal content creation remains underexplored. To bridge this gap, we introduce AV-DiT, a novel and efficient audio-visual diffusion transformer designed to generate high-quality, realistic videos with synchronized audio tracks. To minimize model complexity and computational costs, our AV-DiT utilizes a modality-shared DiT backbone pre-trained on image-only data, with only newly inserted adapters being trainable. This shared backbone facilitates the generation of both audio and video. Specifically, the video branch incorporates a trainable temporal attention layer into a pre-trained DiT block for capturing the temporal consistency for video generation. In addition, a small number of trainable parameters adapt the image-based DiT block to learn the acoustic characteristics for audio generation. An extra shared self-attention block reused from the DiT block, equipped with lightweight parameters, facilitates feature interaction between audio and visual modalities for alignment. Extensive experiments on the datasets demonstrate that our AV-DiT achieves state-of-the-art performance in joint audio-visual generation with significantly fewer tunable parameters. Furthermore, our results highlight that a single shared image generative backbone with modality-specific adaptations is sufficient for constructing a joint audio-video generator.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".