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Two Image Generators Shared a Place to Hand Off the Picture
InstantFusion links frozen generators through a learned common latent interface.
Summary
InstantFusion links frozen generators through a learned common latent interface.
Independently trained image generators do not necessarily organize their internal states in identical ways. InstantFusion learns model-specific encoders and decoders that translate intermediate states through a shared space. Training aligns states from the same image at matched noise levels and synchronizes local denoising velocities. Once learned, the interface can switch between frozen generators without training a separate mapping for every model pair. The authors demonstrate uses in acceleration, preference composition and distillation. It is a proposed interoperability mechanism, with results limited to the generators and settings they evaluated.
Why it matters
InstantFusion links frozen generators through a learned common latent interface.
Limits and context
- Independently trained image generators do not necessarily organize their internal states in identical ways.
Key claims
InstantFusion links frozen generators through a learned common latent interface.
Qualification: Independently trained image generators do not necessarily organize their internal states in identical ways.
Evidence: source-2026-10-12-013
Sources
- arXiv preprint 2610.13944, original v1arXiv / submitting research authors · primary research
Corrections
: Publication note: The October 12, 2026 edition was prepared and published late on October 12 after the scheduled newsroom task could not start. The edition retains its October 12 date and 9:00 AM America/Detroit reveal time. Reports use sources available before that cutoff; actual preparation and image-generation times are preserved in the provenance records.