Inference-Time Scaling of Diffusion Models via Progressive Seed Pruning
Diffusion and flow-matching models dominate conditional image generation, yet inference-time scaling for these models is far less developed than for autoregressive language models. Because final quality is highly sensitive to the initial noise seed, many approaches spend extra compute on seed search or resampling under a black-box reward, but typically maintaining a constant memory footprint throughout inference. We show that relaxing this constraint enables an underexplored inference-time scaling axis: by front-loading exploration, evaluating many seeds early, and pruning aggressively, we can
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “Inference-Time Scaling of Diffusion Models via Progressive S” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
“Fuzzy title match (0.73): “Inference-Time Scaling of Diffusion Models via Progressive S” ≈ “CompVis/stable-diffusion-v1-4””
- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
“Fuzzy title match (0.73): “Inference-Time Scaling of Diffusion Models via Progressive S” ≈ “stabilityai/stable-diffusion-3.5-large””
- PossiblePossibly related (embedding) · 56%DiffusionGemma: 4x faster text generation →
- FuzzySimilar title/name (fuzzy) · 84%xorbitsai/inference →
“Fuzzy title match (0.92): “Inference-Time Scaling of Diffusion Models via Progressive S” ≈ “xorbitsai/inference””
- LinkedLinked via arxiv author · 85%Rogerio Guimaraes →
“Inference-Time Scaling of Diffusion Models via Progressive Seed Pruning”
- LinkedLinked via arxiv author · 85%Pietro Perona →
“Inference-Time Scaling of Diffusion Models via Progressive Seed Pruning”