← Back to all news
arXiv AI By Yuhan Li, Xianfeng Tan, Fangao Zeng, Wenxiang Shang, Pipei Huang, Hao Zhou, Zhiyu Jin, Wenjun Zhang, Bingbing Ni

RAGDiffusion++: From Macro-Retrieval to Micro-Fidelity Alignment for Garment Generation

Read the original on arXiv AI →

RAGDiffusion++ advances garment generation by addressing the high‑frequency texture gap that previous retrieval‑augmented models left unresolved. The approach introduces a dual‑image FLUX architecture trained on a large, complex garment dataset, coupled with a new attribute‑aware reward model that guides reinforcement learning to favor realistic high‑frequency patterns. An adversarial‑regularized RL strategy (AR‑GRPO) further prevents artifact exploitation, ensuring the model samples authentic, detailed garment textures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Aug 11

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation

arXiv:2608. 09226v1 Announce Type: cross Abstract: Efficient text-to-image generation requires both reinforcement-learning (RL)-based reward alignment and few-step distillation, yet these procedures are typically performed sequentially, increasing training cost and risking the loss of reward gains during compression.

By Yuhan Li, Fangao Zeng, Sicong Kang, Mengfei Xu, Hao Zhou, Wei Li, Pipei Huang, Bingbing Ni
diffusionreinforcement-learningefficiencysafety
More like this →
arXiv Machine Learning
Jun 18

The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL

arXiv:2606. 19162v1 Announce Type: new Abstract: Score- and flow-matching models often rely on preference-based reinforcement learning for two purposes: aligning with subjective preferences and, surprisingly, recovering properties such as visual realism and coherent object structure that matching-based training is intended to learn from the data itself.

By Nicolas Beltran-Velez, Felix Friedrich, Zhang Xiaofeng, Reyhane Askari-Hemmat, Xiaochuang Han, Adriana Romero-Soriano, Michal Drozdzal
diffusionreinforcement-learningsafety
More like this →
Hugging Face Trending Papers
Aug 10

RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation

Efficient text-to-image generation requires both reinforcement-learning (RL)-based reward alignment and few-step distillation, yet these procedures are typically performed sequentially, increasing training cost and risking the loss of reward gains during compression. We instead take an RL-native perspective: diffusion RL already generates reward-scored finite-step trajectories, whose intermediate states provide a natural source of distillation supervision rather than a disposable byproduct of sampling.

diffusionreinforcement-learningefficiencysafety
More like this →
arXiv Machine Learning
Jul 15

Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences

arXiv:2607. 12382v1 Announce Type: new Abstract: How can an agent build a structured map of its world from nothing but an ongoing sequence of raw sensory input and its own movements, especially when natural variation means exact sensory patterns rarely repeat?

By Arash Nikzad, Sasan Sarbishegi, Ali Dasmeh, Muhammad Asif, Parsa Gharavi, Erik Husom, Sagar Sen, Andrew B. Lehr, Olivier Penacchio, Ana Clemente, Tristan M. St\"ober
agentsefficiency
More like this →