Network Bending of Diffusion Models for Audio-Visual Generation (2406.19589v1)

Abstract: In this paper we present the first steps towards the creation of a tool which enables artists to create music visualizations using pre-trained, generative, machine learning models. First, we investigate the application of network bending, the process of applying transforms within the layers of a generative network, to image generation diffusion models by utilizing a range of point-wise, tensor-wise, and morphological operators. We identify a number of visual effects that result from various operators, including some that are not easily recreated with standard image editing tools. We find that this process allows for continuous, fine-grain control of image generation which can be helpful for creative applications. Next, we generate music-reactive videos using Stable Diffusion by passing audio features as parameters to network bending operators. Finally, we comment on certain transforms which radically shift the image and the possibilities of learning more about the latent space of Stable Diffusion based on these transforms.

• The paper introduces network bending to modify pre-trained diffusion models, enabling dynamic audio-reactive visual transformations through in-network operations.
• It demonstrates how specific point-wise, tensor, and morphological operations alter image attributes like color balance, saturation, and semantic content.
• The study advances creative multimedia tools by providing fine-grain control over music-reactive video generation, paving the way for enhanced cross-modal synthesis.

Network Bending of Diffusion Models for Audio-Visual Generation

The paper "Network Bending of Diffusion Models for Audio-Visual Generation" by Carmine Emanuele Cella, David Ban, Luke Dzwonczyk, and colleagues provides a detailed examination of a novel approach to integrating audio and visual modalities using generative models, specifically diffusion models. The proposed methodology of "network bending" is applied to Stable Diffusion models to enable artists to generate music-reactive videos with a heightened degree of creative control.

The primary contributions of this paper include the introduction of network bending to diffusion models, the enumeration of various visual effects resulting from different transforms, and the practical demonstration of generating music-reactive videos. These contributions collectively advance the state of artistic tools in the field of audio-visual generation.

Methodology

The authors leverage pre-trained Stable Diffusion models to explore the application of network bending. The core idea behind network bending is to apply transformations, termed operators, within the layers of the diffusion network during image generation. These transformations are categorized into point-wise, tensor-wise, and morphological operations.

Point-Wise Operations:

• Addition of a scalar: $f(x) = x + r$
• Multiplication by a scalar: $f(x) = x \cdot r$
• Hard threshold: $$f(x) = \begin{cases} 1 & \text{if } x \geq r \ 0 & \text{otherwise} \end{cases}$$
• Inversion: $f(x) = \frac{1}{r} - x$

Tensor Operations:

• Rotation and Reflection: Rotation matrices, such as

$$R_1 = \begin{bmatrix} 1 & 0 & 0 & 0 \ 0 & \cos{\theta} & -\sin{\theta} & 0 \ 0 & \sin{\theta} & \cos{\theta} & 0 \ 0 & 0 & 0 & 1 \end{bmatrix}$$

Morphological Transformations:

• Erosion and Dilation: Applied using the Kornia library, producing effects like kaleidoscope visuals.

Experimental Results

The researchers carefully document the visual manifestations induced by various transformations and parameter settings. They conducted a series of experiments to identify the qualitative effects of these operations.

Key Findings:

1. Color Filtering and Saturation: Scalar addition and multiplication transform images by altering color balance and saturation, replicating effects accessible via traditional image editing but achieved here through network layer manipulations.
2. Scene Changes and Semantic Shifts: More profound transformations, such as inversion and rotations, can drastically alter the image content, leading to what the authors term "scene changes" and "semantic shifts." For example, changing a layer transformation can shift an image from depicting a mechanical crane to a bird (crane), exposing the potential underlying structure of the latent space.
3. Fine-Grain Control: Continuous control over visual attributes enables a range of aesthetic effects. For instance, modifying the tensor space allows for effects like dynamic color cycling, effectively providing artists with a nuanced tool for creative expression.
4. Music-Reactive Videos: By parameterizing transformations using audio features (e.g., RMS, spectral features), the researchers generate videos where visuals change responsively to the audio input, advancing beyond static or pre-determined mappings of previous generative systems.

Implications and Future Directions

Practical Implications:

• Creative Tools: The proposed system offers artists a sophisticated yet accessible medium to generate synchronized audio-visual content. The ability to dynamically control and adjust visual characteristics in line with musical input can significantly enhance multimedia artistic endeavors.
• Cross-Modal Synthesis: Insights from this research suggest potential pathways for furthering cross-modal generative models. Enhanced understanding of the interaction between audio features and visual outputs could lead to more intuitive and seamless creative processes.

Theoretical Implications:

• Latent Space Geometry: The observed scene and semantic shifts point to non-trivial geometric properties within the latent space of diffusion models. Understanding these properties can inform broader applications of diffusion models and their inherent structure.

Speculative Future Developments:

1. Machine-Crafted Operators: Moving beyond hand-picked transformations, future work could involve machine learning to craft operators, automating and potentially optimizing the generation process.
2. Enhanced Temporal Control: Implementations that allow artists to define temporal and narrative control points would greatly improve synchronization and thematic continuity in audio-visual projects.
3. Extended Applications: Applying network bending to other generative networks, including text-to-music models, could yield comprehensive creative tools for artists looking to integrate multiple modalities, offering fine-grained control across different aspects of generative output.

Conclusion

The paper lays foundational work for integrating network bending with diffusion models to create sophisticated audio-visual generative systems. This approach offers significant improvements in control and expressiveness over traditional methods, opening new avenues for artistic creativity and multimedia research. The potential for further exploration and refinement of these techniques suggests exciting prospects for both theoretical advancements and practical applications in the domain of generative models.