Diffusion models generate novel samples due to the interaction between denoiser architecture inductive bias and target distribution, with explicit generated distributions derived for linear, polynomial, and bottleneck architectures.
Denoising diffusion implicit models
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
P-Guide achieves single-pass classifier-free guidance in flow matching by modulating the initial latent state and is equivalent to standard CFG under a first-order approximation while cutting latency by half.
Replacing the generic Stable Diffusion VAE with domain-specific MedVAE pretrained on 1.6M medical images improves diffusion-based SR PSNR by 2.91-3.29 dB on knee/brain MRI and chest X-ray, with gains in fine details and VAE quality predicting SR performance (R²=0.67).
citing papers explorer
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Diffusion Models, Denoiser Architecture and Creativity
Diffusion models generate novel samples due to the interaction between denoiser architecture inductive bias and target distribution, with explicit generated distributions derived for linear, polynomial, and bottleneck architectures.
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P-Guide: Parameter-Efficient Prior Steering for Single-Pass CFG Inference
P-Guide achieves single-pass classifier-free guidance in flow matching by modulating the initial latent state and is equivalent to standard CFG under a first-order approximation while cutting latency by half.
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Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution
Replacing the generic Stable Diffusion VAE with domain-specific MedVAE pretrained on 1.6M medical images improves diffusion-based SR PSNR by 2.91-3.29 dB on knee/brain MRI and chest X-ray, with gains in fine details and VAE quality predicting SR performance (R²=0.67).