#image generation

8 results
cs.CV2026

FeatFix: Reuse What You Verify through Local Exact-Feature Correction for Faster Cached Diffusion Inference

Hanshuai Cui, Zhiqing Tang, Zhi Yao +3

FeatFix reuses exact intermediate features computed for verification to locally correct draft outputs in cached diffusion inference, speeding up image and video generation while pr…

#diffusion models#cached inference#feature correction#image generation
cs.LG2026

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation

Alexi Gladstone, Heng Ji, Yilun Du

The paper proposes Explorative Modeling, a new training paradigm that selects the best among multiple candidate generations to improve generative models, adding a third pretraining…

#generative modeling#pretraining#efficiency#image generation
cs.CV2026

Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation

Weiming Zhuang, Jiabo Huang, Jingtao Li +4

The paper introduces Argus-Unified, a compact multimodal model that combines image understanding and generation by leveraging pretrained vision-language models and hybrid visual to…

#unified multimodal model#image understanding#image generation#vision-language models
cs.CV2026

Advanced Image Generation: Negative Prompt Optimization and Latent Classifier Guidance

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

The paper introduces a system that automatically creates optimized negative prompts using a fine‑tuned LLM and guides Stable Diffusion with a latent‑space CNN‑RNN classifier to red…

#image generation#diffusion models#negative prompting#latent guidance
cs.CV2026

VQ-Touch: A Data-Efficient Tactile Generation Framework Across Sensors and Scenarios

Kailin Lyu, Long Xiao, Jianing Zeng +3

The paper presents VQ-Touch, a framework that efficiently generates high‑fidelity tactile images across different sensors and scenarios using a VQ‑GAN based representation and a di…

#tactile sensing#image generation#cross‑sensor generalization#few‑shot learning
cs.CV2026

MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators

Yushi Huang, Xiangxin Zhou, Jun Zhang +2

The paper introduces MeanFlowNFT, a method that applies reinforcement‑learning based reward optimization to MeanFlow generators by learning an instantaneous‑velocity predictor whil…

#meanflow generators#diffusion models#reinforcement learning#few-step sampling
cs.CV2026

Post-Training Pruning for Diffusion Transformers

Chengzhi Hu, Xuewen Liu, Jing Zhang +3

The paper introduces DiT-Pruning, a post‑training pruning method tailored for Diffusion Transformers that uses a new energy‑based saliency metric and clustering‑aware granularity t…

#diffusion models#transformers#model pruning#post‑training compression
cs.CV2026

Feature-Space Guided Diffusion for Realistic Ultrasound Image Synthesis

Marina Domínguez, Nélida Mirabet-Herranz, Valery Naranjo

The paper introduces Feature-Space Candidate Guidance (FSCG), a training‑free sampling technique that uses a frozen ultrasound foundation model to correct diffusion-generated image…

#ultrasound image synthesis#diffusion models#feature-space guidance#medical imaging