Drift-AR achieves 3.8-5.5x speedup in AR-diffusion image models by using entropy to enable entropy-informed speculative decoding and single-step (1-NFE) anti-symmetric drifting decoding.
Laptop-diff: Layer pruning and normalized dis- tillation for compressing diffusion models
5 Pith papers cite this work. Polarity classification is still indexing.
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2ndMatch finetunes pruned diffusion models via second-order Jacobian matching inspired by Finite-Time Lyapunov Exponents to reduce the quality gap with dense models on image generation tasks.
WeiT applies Kronecker-based constraints during pre-training to disentangle size-agnostic knowledge into reusable weight templates and size-specific lightweight scalers for efficient initialization of models with varying depths and widths.
CR-Diff applies block-wise pruning followed by output amplification to diffusion models, improving consistency and fidelity at unseen resolutions while retaining default-resolution performance.
citing papers explorer
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Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting
Drift-AR achieves 3.8-5.5x speedup in AR-diffusion image models by using entropy to enable entropy-informed speculative decoding and single-step (1-NFE) anti-symmetric drifting decoding.
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2ndMatch: Finetuning Pruned Diffusion Models via Second-Order Jacobian Matching
2ndMatch finetunes pruned diffusion models via second-order Jacobian matching inspired by Finite-Time Lyapunov Exponents to reduce the quality gap with dense models on image generation tasks.
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Constraint-based Pre-training: From Structured Constraints to Scalable Model Initialization
WeiT applies Kronecker-based constraints during pre-training to disentangle size-agnostic knowledge into reusable weight templates and size-specific lightweight scalers for efficient initialization of models with varying depths and widths.
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Cross-Resolution Diffusion Models via Network Pruning
CR-Diff applies block-wise pruning followed by output amplification to diffusion models, improving consistency and fidelity at unseen resolutions while retaining default-resolution performance.
- LIFT and PLACE: A Simple, Stable, and Effective Knowledge Distillation Framework for Lightweight Diffusion Models