I2AM: Interpreting Image-to-Image Latent Diffusion Models via Bi-Attribution Maps

Understand

Large-scale diffusion models have made significant advances in image generation, particularly through cross-attention mechanisms.

• While cross-attention has been well-studied in text-to-image tasks, their interpretability in image-to-image (I2I) diffusion models remains underexplored.
• This paper introduces Image-to-Image Attribution Maps (I2AM), a method that enhances the interpretability of I2I models by visualizing bidirectional attribution maps, from the reference image to the generated image and vice versa.
• I2AM aggregates cross-attention scores across time steps, attention heads, and layers, offering insights into how critical features are transferred between images.