Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization
Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. While much work has focused on efficient solvers and samplers, comparatively little attention has been paid to selecting the sampling timesteps themselves. A recent line of work optimizes theoretically derived surrogates for sample quality rather than the quality metric itself. We propose Optimizing Your Sampling (OYS), which instead treats timestep selection as a black-box optimization problem, optimizing the target metric directly with Bayesian o
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “Optimize Your Sampling: Tuned Diffusion Sampling with Bayesi” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
“Fuzzy title match (0.73): “Optimize Your Sampling: Tuned Diffusion Sampling with Bayesi” ≈ “CompVis/stable-diffusion-v1-4””
- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
“Fuzzy title match (0.73): “Optimize Your Sampling: Tuned Diffusion Sampling with Bayesi” ≈ “stabilityai/stable-diffusion-3.5-large””
- PossiblePossibly related (embedding) · 50%Diffusion →
- PossiblePossibly related (embedding) · 46%[Paper] Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling →
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- FuzzyOverlapping authors or contributors · 62%microsoft/ML-For-Beginners →
“Shared author/contributor keys: shin”
- LinkedLinked via arxiv author · 85%Travis Zhang →
“Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization”