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Self-Representation Alignment in Diffusion Models for Video Background Music Generation
Jincheng Zhang, György Fazekas, Charalampos Saitis
TL;DR
Diffusion models can learn insufficiently meaningful internal representations, while their use for symbolic music remains underexplored. U-SRA aligns earlier, higher-noise U-Net representations with later, lower-noise representations during training; for video-to-music generation, it reports stronger retrieval-precision alignment and high music quality, outperforming the state of the art.
Problem
Diffusion models typically supervise final outputs rather than internal representations, and their use for symbolic music remains underexplored.
Source: §I · p. 1Method
U-SRA trains a diffusion U-Net with an alignment loss that guides earlier, higher-noise representations toward later, lower-noise representations.
Source: §I · p. 1Results
Takeaways & Limitations
The reported results support using self-representation alignment in diffusion U-Nets for video-conditioned symbolic music without external representation components.
Source: §V · p. 5
Abstract
from arXiv · showhide
Diffusion models have been widely used for various generative tasks. However, recent studies have demonstrated that the internal representations learned in the diffusion models' denoising process are insufficiently meaningful, which can hinder the generation performance. To address this, we propose a novel diffusion U-Net architecture enhanced by explicitly aligning the weaker representation from the earlier layer conditioned on higher noise with the better representation from the later layer conditioned on lower noise during training, which we use for the challenging multimodal task of video background music generation. Our diffusion U-Net Self-Representation Alignment (U-SRA) employs an alignment loss to effectively guide our diffusion model to learn good internal representations, thereby enabling it to produce higher-quality symbolic music conditioned on video features. We employ a cross-attention mechanism to synchronize music with video segments. Experiments on a benchmark video-music dataset demonstrate that the proposed U-SRA approach for video background music generation surpasses the state-of-the-art across key evaluation metrics, including video-music alignment and overall music quality.
I. INTRODUCTION
Video-conditioned symbolic music generation offers an editable, interpretable alternative to audio synthesis, but diffusion models for symbolic music remain underexplored and can learn weak internal representations. The paper introduces U-SRA to align representations across U-Net layers and reports stronger video–music alignment than the current state of the art.
I. INTRODUCTION
- Video-conditioned symbolic music generation targets creators who struggle to find matching music, while symbolic outputs remain interpretable and editable in standard digital audio workstations.Symbolic music is represented by discrete tokens for attributes such as pitch, duration, and velocity.Source: §I · p. 1
- Transforming discrete MIDI into pianorolls enables direct diffusion training on symbolic music without an auxiliary VAE.Pianorolls resemble images, with time on the horizontal axis and pitch on the vertical axis.Source: §I · p. 1
- U-SRA uses an alignment loss to guide earlier, higher-noise U-Net representations toward later, lower-noise representations.The paper identifies this as its first explicit optimization of diffusion U-Net internal representations via alignment loss.Source: §I · p. 1
- Moderately deep layers tend to provide more effective guidance to early layers during self-representation alignment.The paper empirically investigates different layer pairings.Source: §I · p. 1
- The proposed diffusion model with SRA outperforms the current state of the art for video-to-music generation on retrieval-precision measures of video–music alignment.The paper identifies retrieval precision as its video–music alignment measure.Source: §I · p. 1
A. Video-to-Music Generation
Prior video-to-music methods vary in the kinds of video-music correspondence they capture and in whether they generate symbolic music or audio. Diff-BGM addresses symbolic generation, but shares diffusion models’ suboptimal internal-representation quality.
A. Video-to-Music Generation
- Early video-to-music methods often focused on simple settings such as dance or instrumental-performance videos.Foley Music aligns generated music with body motions in videos.Source: A. Video-to-Music Generation · p. 2
- CMT primarily captures rhythmic correspondence in freestyle videos, whereas V-MusProd guides generation with semantic, color, and motion features.V-MusProd enables higher-level semantic alignment between video content and background music.Source: A. Video-to-Music Generation · p. 2
- SONIQUE generates audio music but struggles with precise temporal alignment, while Diff-BGM generates symbolic music conditioned on videos.Diff-BGM inherits the limitation of suboptimal internal representations in diffusion models.Source: A. Video-to-Music Generation · p. 2
B. Representation Alignment
Representation-alignment approaches can improve diffusion-model representation learning, but prior approaches face practical or architectural constraints. The paper presents its method as the first self-representation alignment approach for diffusion U-Nets.
B. Representation Alignment
- REPA aligns diffusion-transformer states with large-scale pretrained representations, but reliance on external representation models adds complexity that constrains practical application.DINOv2 is given as an example of an external representation model.Source: B. Representation Alignment · p. 2
- Self-representation alignment in diffusion transformers does not directly transfer to U-Nets because downsampling creates representations with mismatched spatial dimensions.The cited distinction is that transformers maintain uniform resolution across layers.Source: B. Representation Alignment · p. 2
- The paper presents its method as the first self-representation alignment approach for diffusion U-Nets and reports consistently promising evaluation results for video-to-music generation.The method is described as generating music from videos.Source: B. Representation Alignment · p. 2
III. METHOD
The method adds self-representation alignment (SRA) to a diffusion U-Net for video-conditioned pianoroll generation. It aligns an earlier, higher-noise student representation with a later, lower-noise teacher representation while using video features through cross-attention.
III. METHOD
- The student and teacher representations are aligned after resizing the student feature to match the teacher’s spatial resolution and projecting it through a lightweight MLP.The alignment uses a smooth-ℓ1 distance.Source: §III · p. 2
- The student and teacher inputs can differ by timestep interval k; the experiments use 0–200 because excessively large intervals can prioritize alignment over generation.The stated interval is consistent with [12].Source: §III · p. 2
- SRA aligns an earlier student layer at higher noise with a later teacher layer at lower noise, with the teacher updated by EMA and gradients stopped through it.The teacher provides the target while gradients flow only through the student.Source: §III · p. 2, Figure 1 · p. 3
- The training objective combines diffusion and SRA losses, with λ balancing the generation and alignment objectives.The diffusion model is trained to reverse a Gaussian-noise process that progressively corrupts clean samples.Source: §III · pp. 2–3
- The model conditions pianoroll generation on visual and language features, using noisy pianoroll latents as cross-attention queries and video features as keys and values.The features are extracted from video and captions, and the conditioning setup follows baseline.Source: §III · p. 3
A. Dataset
The dataset uses piano MIDI aligned semantically and temporally with videos, represented as image-like pianoroll segments.
A. Dataset
- BGM909 contains 909 piano MIDI files aligned semantically and temporally with corresponding videos.The music is divided into eight-bar segments for training.Source: A. Dataset · p. 3
- Each eight-bar segment is represented as a pianoroll with shape (2, 128, 128), separating onsets from sustains to make note boundaries explicit.Source: A. Dataset · p. 3
- The pianoroll’s horizontal axis represents 128 time steps, while its vertical axis spans pitches 0 to 127.The diffusion model is trained on these pianorolls.Source: A. Dataset · p. 3
B. Experimental Setup
The experimental setup uses 1,000 diffusion steps and trains the proposed model for 100 epochs.
B. Experimental Setup
- The diffusion process uses N = 1000 steps, and training runs for 100 epochs with Adam at a fixed learning rate of 5 × 10−5.Source: B. Experimental Setup · p. 3
- VideoCLIP extracts visual features, while BLIP generates captions encoded by the pre-trained language model.Source: B. Experimental Setup · p. 3
- The visual and language encoders remain frozen during training, following previous work for a fair comparison.Source: B. Experimental Setup · p. 3
C. Results & Discussion
U-SRA (5-15) outperforms Diff-BGM on the reported music-quality metrics and video–music correspondence. Layer-pair ablations and subjective listening results further favor the 5th-student/15th-teacher pairing.
C. Results & Discussion
- Retrieval precision uses candidate sets of 64 tracks and reports P@5, P@10, and P@20 for semantic and temporal video–music alignment.A generated track is counted as aligned when its ground-truth music ranks within the top K candidates.Source: C. Results & Discussion · pp. 3–4
- U-SRA (5-15) consistently outperforms Diff-BGM on PCHE, GPS, and video–music correspondence, with PCHE and GPS practically matching real-data values.Table I evaluates music quality and correspondence; higher retrieval precision is better, while PCHE and GPS closer to real data indicate better results.Source: C. Results & Discussion · p. 4, Table I · p. 4
- Aligning the student’s 5th layer with the teacher’s 15th yields the best ablation results; teacher layers 16 or 20 provide less effective guidance.The authors suggest intermediate layers offer semantically rich guidance, while excessively deep layers may focus more on high-frequency image details.Source: C. Results & Discussion · p. 4
- U-SRA (5-15) scores highest on most subjective metrics, with Richness comparable to Diff-BGM; its highest perceptual Rhythm score corresponds to improved GPS.The listening test involved 25 participants rating anonymized audio renderings on five criteria.Source: C. Results & Discussion · pp. 4–5, Table II · p. 5
V. CONCLUSION AND FUTURE WORK
The paper presents U-SRA for video-to-music generation, aligning representations within a diffusion U-Net without external representation components. Reported evaluations show high music quality and strong video–music alignment, while evaluation on additional datasets remains future work.
V. CONCLUSION AND FUTURE WORK
- U-SRA aligns an earlier high-noise layer’s latent representation with a later low-noise layer’s representation, without external representation components.The authors describe this as progressively strengthening representation learning.Source: §V · p. 5
- The proposed model generates symbolic music with high music quality and strong video–music alignment, outperforming the state-of-the-art method.Source: §V · p. 5
- Generalization remains to be further validated by applying U-SRA to additional video–music datasets.The authors state that the method does not rely on dataset-specific assumptions.Source: §V · p. 5