D2PO learns better low-NFE diffusion timestep schedules and CFG weights via DPO on a score-based energy with a dynamic denser-schedule preference target.
Learn to guide your diffusion model
5 Pith papers cite this work. Polarity classification is still indexing.
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FlowBender introduces closed-loop training that lets conditional flow models learn correction policies from their own task-specific alignment errors, outperforming supervised and guidance baselines on fidelity and plausibility.
An information-theoretic method optimizes CFG schedules in diffusion models by targeting a reference consistency-coverage trade-off via trajectory-level estimates from samples.
VAGS adapts the CFG scale at each ODE step using velocity alignment signals to raise structural fidelity in editing and sample quality in generation over fixed-scale baselines.
Diffusion model climate emulators provide probability density estimates that allow likelihood calculations and odds-ratio-based importance sampling for extreme events such as tropical cyclones.
citing papers explorer
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D2PO: Optimizing Diffusion Samplers via Dynamic Preference
D2PO learns better low-NFE diffusion timestep schedules and CFG weights via DPO on a score-based energy with a dynamic denser-schedule preference target.
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FlowBender: Feedback-Aware Training for Self-Correcting Conditional Flows
FlowBender introduces closed-loop training that lets conditional flow models learn correction policies from their own task-specific alignment errors, outperforming supervised and guidance baselines on fidelity and plausibility.
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Information-Theoretic Classifier-Free Guidance with Adaptive Schedule Optimization
An information-theoretic method optimizes CFG schedules in diffusion models by targeting a reference consistency-coverage trade-off via trajectory-level estimates from samples.
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VAGS: Velocity Adaptive Guidance Scale for Image Editing and Generation
VAGS adapts the CFG scale at each ODE step using velocity alignment signals to raise structural fidelity in editing and sample quality in generation over fixed-scale baselines.
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Towards accurate extreme event likelihoods from diffusion model climate emulators
Diffusion model climate emulators provide probability density estimates that allow likelihood calculations and odds-ratio-based importance sampling for extreme events such as tropical cyclones.