P-Flow stabilizes flow-matching models for inverse problems via proxy gradients and Gaussian spherical projections, avoiding long-chain differentiation while maintaining prior consistency.
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AuxPath-FM extends flow matching to arbitrary auxiliary distributions while preserving the continuity equation and marginal training objective.
P-Guide achieves single-pass classifier-free guidance in flow matching by modulating the initial latent state and is equivalent to standard CFG under a first-order approximation while cutting latency by half.
citing papers explorer
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P-Flow: Proxy-gradient Flows for Linear Inverse Problems
P-Flow stabilizes flow-matching models for inverse problems via proxy gradients and Gaussian spherical projections, avoiding long-chain differentiation while maintaining prior consistency.
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Flow Matching with Arbitrary Auxiliary Paths
AuxPath-FM extends flow matching to arbitrary auxiliary distributions while preserving the continuity equation and marginal training objective.
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P-Guide: Parameter-Efficient Prior Steering for Single-Pass CFG Inference
P-Guide achieves single-pass classifier-free guidance in flow matching by modulating the initial latent state and is equivalent to standard CFG under a first-order approximation while cutting latency by half.