Normalizing flows are constructed by learning the velocity of a stochastic interpolant via a quadratic loss derived from its probability current, yielding an efficient ODE-based alternative to diffusion models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =
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DiHAL uses geometry proxies to pick where to replace the lower layers of a pretrained transformer with a diffusion bridge for hidden-state reconstruction, improving over token-level diffusion baselines on 8B models.
Variable codebook sizes that increase along the sequence in visual tokenizers reduce generation FID scores significantly for autoregressive models on ImageNet.
S2C-3D reconstructs complete high-fidelity 3D scenes from as few as 6-8 images by finetuning a diffusion model on scene data, applying consistency-conditioned sampling, and planning trajectories for full coverage.
SSDMs introduce an intrinsic score-based diffusion framework on the Fubini-Study manifold to sample quantum pure-state ensembles without classical re-preparation.
LHSD uses spectral filtering on the log-density Hessian to isolate tangent directions from noise and estimate local intrinsic dimension scalably via Stochastic Lanczos Quadrature.
Image editing models fail zero-shot visual planning on abstract mazes and queen puzzles but generalize after finetuning, yet still cannot match human zero-shot efficiency.
CASA uses spectral density to arbitrate between preserving the target model's manifold and restoring LoRA alignment, mitigating style degradation and structural collapse in distilled video diffusion models.
Near-reversible Runge-Kutta diffusion ODE solvers with vector-field smoothing improve stability and edit fidelity for large changes in text-guided image editing compared to exactly reversible alternatives.
citing papers explorer
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Building Normalizing Flows with Stochastic Interpolants
Normalizing flows are constructed by learning the velocity of a stochastic interpolant via a quadratic loss derived from its probability current, yielding an efficient ODE-based alternative to diffusion models.
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Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement
DiHAL uses geometry proxies to pick where to replace the lower layers of a pretrained transformer with a diffusion bridge for hidden-state reconstruction, improving over token-level diffusion baselines on 8B models.
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Taming the Entropy Cliff: Variable Codebook Size Quantization for Autoregressive Visual Generation
Variable codebook sizes that increase along the sequence in visual tokenizers reduce generation FID scores significantly for autoregressive models on ImageNet.
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Sparse-to-Complete: From Sparse Image Captures to Complete 3D Scenes
S2C-3D reconstructs complete high-fidelity 3D scenes from as few as 6-8 images by finetuning a diffusion model on scene data, applying consistency-conditioned sampling, and planning trajectories for full coverage.
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Stochastic Schr\"odinger Diffusion Models for Pure-State Ensemble Generation
SSDMs introduce an intrinsic score-based diffusion framework on the Fubini-Study manifold to sample quantum pure-state ensembles without classical re-preparation.
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Local Hessian Spectral Filtering for Robust Intrinsic Dimension Estimation
LHSD uses spectral filtering on the log-density Hessian to isolate tangent directions from noise and estimate local intrinsic dimension scalably via Stochastic Lanczos Quadrature.
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Probing Visual Planning in Image Editing Models
Image editing models fail zero-shot visual planning on abstract mazes and queen puzzles but generalize after finetuning, yet still cannot match human zero-shot efficiency.
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Exploring Data-Free LoRA Transferability for Video Diffusion Models
CASA uses spectral density to arbitrate between preserving the target model's manifold and restoring LoRA alignment, mitigating style degradation and structural collapse in distilled video diffusion models.
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Stable and Near-Reversible Diffusion ODE Solvers for Image Editing
Near-reversible Runge-Kutta diffusion ODE solvers with vector-field smoothing improve stability and edit fidelity for large changes in text-guided image editing compared to exactly reversible alternatives.