InfiniteDiffusion adapts diffusion models to produce infinite, seed-consistent, high-fidelity terrain with procedural-noise-like access and 9x speed over prior methods.
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SubFlow restores full mode coverage in one-step flow matching by conditioning on sub-modes from semantic clustering, yielding higher diversity on ImageNet-256 while preserving FID.
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InfiniteDiffusion: Bridging Learned Fidelity and Procedural Utility for Open-World Terrain Generation
InfiniteDiffusion adapts diffusion models to produce infinite, seed-consistent, high-fidelity terrain with procedural-noise-like access and 9x speed over prior methods.
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SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation
SubFlow restores full mode coverage in one-step flow matching by conditioning on sub-modes from semantic clustering, yielding higher diversity on ImageNet-256 while preserving FID.