EigDiff proposes using eigensamples to train diffusion models on much smaller datasets while maintaining performance. By computing latent eigenvectors via PCA in a pre‑trained CLIP space, the method selects a representative training set that includes exemplars close to these eigenvectors and diverse images. Experiments show that EigDiff can reduce training data size by up to 14× and accelerate subset selection by up to 1607× with only marginal performance loss.
arXiv:2407. 01718v2 Announce Type: replace-cross Abstract: Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis.
The paper investigates how variational autoencoders (VAEs) can be made more learnable in latent diffusion by analyzing diffusability through spectral properties. It introduces the Spectrum Matching Hypothesis, which posits that latents with better diffusability should have a flattened power‑law power spectral density (Encoding Spectrum Matching) and preserve frequency‑to‑frequency semantic correspondence via the decoder (Decoding Spectrum Matching). The authors implement these ideas by matching PSDs between images and latents and applying shared spectral masking, demonstrating improved diffusion generation on CelebA and ImageNet and extending the approach to representation alignment with a DoG‑based method.
arXiv:2409. 02426v5 Announce Type: replace Abstract: Despite their empirical success across a wide range of generative tasks, the fundamental principles underlying the ability of diffusion models to learn data distributions are poorly understood.
arXiv:2602. 02908v2 Announce Type: replace-cross Abstract: Diffusion models trained on different, non-overlapping subsets of a dataset often produce strikingly similar outputs when given the same noise seed.
arXiv:2609.08253v1 Announce Type: new Abstract: Diffusion models have shown remarkable performance on diverse generation tasks. Recent work finds that imposing representation alignment on the hidden...
arXiv:2603.01623v2 Announce Type: replace Abstract: Diffusion models have become the dominant tool for high-fidelity image and video generation, yet are critically bottlenecked by their inference spe...
arXiv:2609.15643v1 Announce Type: new Abstract: Flow-matching diffusion models have recently emerged as a strong paradigm for high-fidelity visual generation. However, their prohibitively high fine-t...
Latent diffusion models achieve strong generative performance by operating in a compressed latent space produced by a variational autoencoder (VAE). However, it remains unclear whether all latent channels contribute equally to the diffusion process, or whether significant redundancy exists.
ORCA (Orthogonal Residual Compositional Alignment) is a method that improves text-to-image diffusion models by aligning the diffusion transformer’s latent space with a low‑rank target derived from a frozen visual encoder. It introduces an auxiliary loss that uses a predictor with an orthogonal basis parameterised by a learned residual between T5 and CLIP embeddings, providing a prompt‑dependent signal for selecting the visual readout subspace. Experiments on three diffusion‑transformer backbones show that ORCA improves FID and GenEval scores, especially on attribute binding, spatial relations, and multi‑object prompts, without adding inference‑time cost.
arXiv:2610.07284v1 Announce Type: cross Abstract: Spectral methods are central to high-dimensional statistics and machine learning, underlying procedures for covariance estimation, matrix denoising,...
The paper introduces MedSegLatDiff, a diffusion-based framework that combines a variational autoencoder (VAE) with a latent diffusion model for medical image segmentation. By compressing images into a low-dimensional latent space, the method reduces noise and speeds up training, while a weighted cross‑entropy loss preserves tiny structures such as small nodules. Evaluated on ISIC‑2018, CVC‑Clinic, and LIDC‑IDRI datasets, MedSegLatDiff achieves state‑of‑the‑art Dice and IoU scores, generates diverse segmentation hypotheses, and produces confidence maps that enhance interpretability and reliability for clinical deployment.
The paper investigates why diffusion models, despite their practical success, can fully memorize training data when optimal. It shows that as training proceeds, the denoising objective causes overfitting at intermediate noise levels, creating a gap between validation and training performance. In a 2D toy model, this gap arises from the interplay between model error and the data distribution’s support density, leading to a smooth, generalizing flow field rather than exact recall of training points. The study also reveals that predictions from intermediate inference states become increasingly similar to training data in feature space, even as overall similarity to validation data remains unchanged.