arXiv AI

Unified Backbone Refinement for Diffusion Models via Internal-Latent Analysis

arXiv:2607. 09753v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success across diverse domains, with performance closely related to the denoising backbones that parameterize the score function.

Hugging Face Trending Papers

Jun 22

PeLAP-A: Adaptive Latent Pruning for Lightweight Latent Diffusion Models

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.

diffusion efficiency

arXiv Computer Vision

4d ago

Guided Trajectory Optimization with Sparse Scaling for Test-Time Diffusion

arXiv:2605.21907v2 Announce Type: replace Abstract: Test-Time Scaling (TTS) paradigm offers a promising perspective for enhancing the generation performance of diffusion models. However, current solu...

By Gang Dai, Yining Huang, Yiming Xia, Guohao Chen, Shuaicheng Niu

diffusion

arXiv AI

Jul 7

Steering Optimisation Trajectories in Diffusion Representation Learning

arXiv:2607. 05319v1 Announce Type: cross Abstract: We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures.

By Rajat Rasal, Avinash Kori, Tian Xia, Ben Glocker

diffusion computer-vision benchmarks

arXiv Computer Vision

Sep 1

EMAG: Self-Rectifying Diffusion Sampling with Exponential Moving Average Guidance

arXiv:2512.17303v3 Announce Type: replace Abstract: In diffusion and flow-matching generative models, guidance techniques are widely used to improve sample quality and consistency. Classifier-free gu...

By Ankit Yadav, Ta Duc Huy, Lingqiao Liu

llms diffusion

arXiv Machine Learning

Jun 19

On the Redundancy of Timestep Embeddings in Diffusion Models

arXiv:2606. 20416v1 Announce Type: new Abstract: Diffusion models rely heavily on explicit timestep embeddings to modulate the denoising process across various noise scales.

By Jos\'e A. Ch\'avez

llms rag diffusion benchmarks

arXiv AI

Aug 5

PRISMA: Improving the Accuracy-Latency Frontier of Diffusion-based PDE Solvers Using Physics-Informed Spectral Attention

arXiv:2512. 01370v2 Announce Type: replace-cross Abstract: Diffusion-based solvers for partial differential equations (PDEs) are often bottle-necked by slow gradient-based test-time optimization routines that use PDE residuals for loss guidance.

By Medha Sawhney, Abhilash Neog, Mridul Khurana, Anuj Karpatne

diffusion benchmarks

arXiv AI

Jul 3

Subliminal Clocks: Latent Time Modelling in Diffusion Language Models

arXiv:2607. 01774v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have recently emerged as a promising alternative to autoregressive models.

By Maximo Rulli (Sapienza University of Rome), Thomas Fontanari (Sapienza University of Rome), Simone Petruzzi (Sapienza University of Rome), Federico Alvetreti (Sapienza University of Rome), Giorgio Strano (Sapienza University of Rome), Donato Crisostomi (Sapienza University of Rome), Giorgos Nikolaou (EPFL), Tommaso Mencattini (EPFL), Andrea Santilli (Independent researcher), Emanuele Rodol\`a (Sapienza University of Rome), Simone Scardapane (Sapienza University of Rome), Alessio Devoto (Independent researcher)

llms diffusion

arXiv AI

Jun 17

Rethinking Cross-Layer Information Routing in Diffusion Transformers

arXiv:2605. 20708v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) have become a de facto backbone of modern visual generation, and nearly every major axis of their design -- tokenization, attention, conditioning, objectives, and latent autoencoders -- has been extensively revisited.

By Chao Xu, Maohua Li, Qirui Li, Yixuan Xu, Yanke Zhou, Yunhe Li, Cuifeng Shen, Hanlin Tang, Kan Liu, Tao Lan, Lin Qu, Shao-Qun Zhang

llms diffusion nlp fine-tuning efficiency safety

arXiv AI

Jul 1

Look But Don't Touch with Sparse Autoencoders for Unlearning in Diffusion Models

arXiv:2606. 31699v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have recently been proposed as interpretable tools for concept-level manipulation, under the assumption that isolated features can serve as controllable intervention points.

By Enrico Cassano, Riccardo Renzulli, Rayyan Ahmed, Marco Grangetto, Stephan Alaniz

rag diffusion robotics safety

arXiv Machine Learning

Jun 8

Generalization of Diffusion Models Arises with a Balanced Representation Space

arXiv:2512. 20963v3 Announce Type: replace Abstract: Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective.

By Zekai Zhang, Xiao Li, Xiang Li, Lianghe Shi, Meng Wu, Molei Tao, Qing Qu

diffusion

arXiv Computation and Language

4d ago

E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models

arXiv:2609.37533v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically...

By Arseny Ivanov, Alexander Kolesov, Alexander Korotin, Ivan Oseledets, Mikhail Goncharov

llms diffusion multimodal benchmarks

arXiv Machine Learning

Jul 14

Diversify Diffusion with Temperature Sampling and Variance-Corrective Time Shifting

arXiv:2607. 10853v1 Announce Type: cross Abstract: Diffusion models faithfully reproduce their training distribution, but also inherit its imbalances and leave rare or under-represented modes hard to reach.

By Peizhuo Li, Emre Aksan, Alexandru-Eugen Ichim, Thabo Beeler, Olga Sorkine-Hornung

diffusion

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