# SonicDiffusion: Audio-Driven Image Generation and Editing with Pretrained Diffusion Models

arXiv: https://arxiv.org/abs/2405.00878 · HTML: https://mlsanity.com/paper/2405.00878/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 28 of its 42 references that are in the map.
- First posted on arXiv 1 May 2024.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 28 of 28)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [AudioCLIP: Extending CLIP to Image, Text and Audio](https://mlsanity.com/paper/2106.13043/) (2021)
- [Self-Supervised MultiModal Versatile Networks](https://mlsanity.com/paper/2006.16228/) (2020)
- [Visually Indicated Sounds](https://mlsanity.com/paper/1512.08512/) (2015)
- [Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation](https://mlsanity.com/paper/2309.16429/) (2023)
- [Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment](https://mlsanity.com/paper/2303.17490/) (2023)
- [The Power of Sound (TPoS): Audio Reactive Video Generation with Stable Diffusion](https://mlsanity.com/paper/2309.04509/) (2023)
- [GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation](https://mlsanity.com/paper/2303.10056/) (2023)
- [Learning Visual Styles from Audio-Visual Associations](https://mlsanity.com/paper/2205.05072/) (2022)

## Built on by (most cited first, 15 of 15)

- [AV-Link: Temporally-Aligned Diffusion Features for Cross-Modal Audio-Video Generation](https://mlsanity.com/paper/2412.15191/) (2024)
- [Diffusion-Based Visual Art Creation: A Survey and New Perspectives](https://mlsanity.com/paper/2408.12128/) (2024)
- [Zero-Shot Audio-Visual Editing via Cross-Modal Delta Denoising](https://mlsanity.com/paper/2503.20782/) (2025)
- [Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generation](https://mlsanity.com/paper/2501.15878/) (2025)
- [FineGRAIN: Evaluating Failure Modes of Text-to-Image Models with Vision Language Model Judges](https://mlsanity.com/paper/2512.02161/) (2025)
- [GPS as a Control Signal for Image Generation](https://mlsanity.com/paper/2501.12390/) (2025)
- [Promptus: Can Prompts Streaming Replace Video Streaming with Stable Diffusion](https://mlsanity.com/paper/2405.20032/) (2024)
- [Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting](https://mlsanity.com/paper/2503.06860/) (2025)
- [FlowBind: Efficient Any-to-Any Generation with Bidirectional Flows](https://mlsanity.com/paper/2512.15420/) (2025)
- [Seeing Soundscapes: Audio-Visual Generation and Separation from Soundscapes Using Audio-Visual Separator](https://mlsanity.com/paper/2504.18283/) (2025)
- [SounDiT: Geo-Contextual Soundscape-to-Landscape Generation](https://mlsanity.com/paper/2505.12734/) (2025)
- [Audio-Guided Visual Editing with Complex Multi-Modal Prompts](https://mlsanity.com/paper/2508.20379/) (2025)
- [SeeingSounds: Learning Audio-to-Visual Alignment via Text](https://mlsanity.com/paper/2510.11738/) (2025)
- [Asymmetric Hierarchical Anchoring for Robust Audio-Visual Cross-Modal Generalization](https://mlsanity.com/paper/2602.03570/) (2026)
- [Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models](https://mlsanity.com/paper/2606.14756/) (2026)

## Abstract (arXiv)

We are witnessing a revolution in conditional image synthesis with the recent success of large scale text-to-image generation methods. This success also opens up new opportunities in controlling the generation and editing process using multi-modal input. While spatial control using cues such as depth, sketch, and other images has attracted a lot of research, we argue that another equally effective modality is audio since sound and sight are two main components of human perception. Hence, we propose a method to enable audio-conditioning in large scale image diffusion models. Our method first maps features obtained from audio clips to tokens that can be injected into the diffusion model in a fashion similar to text tokens. We introduce additional audio-image cross attention layers which we finetune while freezing the weights of the original layers of the diffusion model. In addition to audio conditioned image generation, our method can also be utilized in conjuction with diffusion based editing methods to enable audio conditioned image editing. We demonstrate our method on a wide range of audio and image datasets. We perform extensive comparisons with recent methods and show favorable performance.