arXiv AI July 9, 2026 By Jinho Chang, Changsun Lee, Hyungjin Chung, Jong Chul Ye

ContrastiveCFG: Guiding Diffusion Sampling by Contrasting Positive and Negative Concepts

arXiv:2411. 17077v2 Announce Type: replace-cross Abstract: As Classifier-Free Guidance (CFG) has proven effective in conditional diffusion model sampling for improved condition alignment, many applications use a negated CFG term as a Negative Prompting (NP) to filter out unwanted features from samples.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 10

MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.

By Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali, Farzan Farnia

diffusion fine-tuning benchmarks safety

arXiv AI

Jun 12

The Safety-Aware Denoiser for Text Diffusion Models

arXiv:2605. 08116v2 Announce Type: replace-cross Abstract: Recent work on text diffusion models offers a promising alternative to autoregressive generation, but controlling their safety remains underexplored.

By Amman Yusuf, Zhejun Jiang, Mijung Park

diffusion safety

arXiv Machine Learning

Jun 25

Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model

arXiv:2507. 09052v3 Announce Type: replace-cross Abstract: Training data for class-conditional image synthesis often exhibit a long-tailed distribution with limited amount of images for tail classes.

By Fang Chen, Alex Villa, Gongbo Liang, Fuxing Li, Xiaoyi Lu, Meng Tang

llms diffusion safety

arXiv AI

Jun 2

IDLM: Inverse-distilled Diffusion Language Models

arXiv:2602. 19066v2 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have recently achieved strong results in text generation.

By David Li, Nikita Gushchin, Dmitry Abulkhanov, Eric Moulines, Ivan Oseledets, Maxim Panov, Alexander Korotin

llms diffusion efficiency

arXiv Machine Learning

Jul 21

Semi-Supervised Conditional Diffusion via Label Augmentation

arXiv:2607. 16685v1 Announce Type: cross Abstract: Conditional diffusion models have become a powerful and flexible framework for learning complex conditional distributions from labeled data.

By Jin Su, Yuan Gao, Yong Zhou, Jian Huang

diffusion benchmarks

arXiv Machine Learning

Jul 17

Advanced Image Generation: Negative Prompt Optimization and Latent Classifier Guidance

arXiv:2607. 14580v1 Announce Type: cross Abstract: We present a novel system that integrates negative prompt optimization via a fine-tuned sequence-to-sequence LLM and latent-space classifier guidance to improve the quality of images generated by Stable Diffusion.

By Vaddi Charan Sai Nandan Reddy, Harini B, Chandana M S

llms diffusion fine-tuning

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MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

The Safety-Aware Denoiser for Text Diffusion Models

Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model

IDLM: Inverse-distilled Diffusion Language Models

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Advanced Image Generation: Negative Prompt Optimization and Latent Classifier Guidance