InstantFusion: A Shared Latent Interface for Heterogeneous Image Generators

Modern image generation relies heavily on heterogeneous diffusion and flow-matching models, yet aligning their distinct latent representations and denoising dynamics remains a significant challenge. The source paper introduces InstantFusion, a lightweight shared latent interface designed to bridge the denoising trajectories of these unaligned, frozen generators. The core methodology leverages model-specific Latent AutoEncoders (LAEs) to map intermediate states from various source generators into a unified, shared latent space, which are then decoded into the target generator's latent space. This shared interface is optimized using two primary objectives: aligning intermediate states derived from the same source image at corresponding noise levels, and synchronizing their local denoising velocities. The authors report that this

framework enables seamless denoising transitions across heterogeneous architectures without requiring joint training or fine-tuning of the base generators. For M.Tech researchers, this paper provides a robust foundation for studying cross-model compatibility and latent-space dynamics. However, because the full training complexity and scaling limits across highly diverse model families are not fully detailed in the brief, implementation support will focus on structuring the LAE architecture, formulating the velocity synchronization loss, and setting up comparative simulation pipelines.

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