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Geração de Imagem

Papers, modelos e datasets em alta no Hugging Face, além do blog oficial — com leitura editorial em português.

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Blog LLMs & Texto

TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning

arXiv:2606.31166v1 Announce Type: new Abstract: Text-attributed graphs (TAGs), where each node carries a natural language description, require models to jointly reason over text and graph topology. Existing approaches often handle the two modalities separately: graph neural networks operate on shallow text features, while hybrids of LLMs and graphs use the language model mainly as a text encoder and delegate structure learning to a separate graph module. We propose method that unifies textual re...

01.07.2026
Blog LLMs & Texto

DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning

arXiv:2606.30694v1 Announce Type: new Abstract: Traffic signal control at urban intersections inherently introduces stop-and-go behavior, resulting in increased delays and reduced traffic efficiency, especially under high traffic demand. With the emergence of connected and automated vehicles (CAVs), trajectory-level coordination has emerged as a high-potential strategy to augment or transcend conventional phase-based management. This paper proposes DSIP (Diffusion-model-based Signal-free Interse...

01.07.2026
Blog LLMs & Texto

SyncCache: Exploiting Asymmetric Dynamics for Fast Audio-Driven Portrait Animation

arXiv:2606.30849v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have significantly advanced audio-driven portrait animation, but their high computational cost leads to substantial inference latency. Although training-free diffusion caching accelerates inference significant, existing methods are primarily developed for text-conditioned generation and overlook the spatial and modality imbalances inherent in audio-driven portrait animation. In this paper, we propose SyncCache, a train...

01.07.2026
Blog Geração de Imagem

Diffusion-based 4D Trajectory Prediction and Distributed Control for UAV Swarms

arXiv:2606.31197v1 Announce Type: new Abstract: Accurate 4D trajectory prediction and closed-loop tracking are essential for Unmanned Aerial Vehicle (UAV) swarms to achieve safe and efficient operations in complex low-altitude environments such as urban airspaces, industrial sites, and indoor facilities. However, this task remains challenging due to intrinsic nonlinearity of UAV swarm dynamics and strict real-time constraints of swarm formation control. To address these challenges, we propose a ...

01.07.2026
Google launches Nano Banana 2 Lite for fast AI images and Gemini Omni Flash for video via API
Blog LLMs & Texto

Google launches Nano Banana 2 Lite for fast AI images and Gemini Omni Flash for video via API

Google adds two new generative AI models. Nano Banana 2 Lite generates images in four seconds at $0.034 a pop. Gemini Omni Flash brings video generation and editing via text prompts to the API for the first time. Google recommends chaining both models to go from a quick image to an animated video. The article Google launches Nano Banana 2 Lite for fast AI images and Gemini Omni Flash for video via API appeared first on The Decoder .

30.06.2026
Blog Geração de Imagem

Constrained Tabular Diffusion for Finance

arXiv:2606.28674v1 Announce Type: new Abstract: Generative models in finance face the dual challenge of producing realistic data while satisfying strict regulatory and economic objectives, a requirement that standard tabular diffusion models cannot provide. To address this difficulty, we introduce Constrained Tabular Diffusion for Finance (CTDF), a novel integration of sampling-time feasibility operations with mixed-type tabular diffusion in financial applications. By incorporating a training-fr...

30.06.2026
Blog Geração de Imagem

DiffRGD: An Inference-Time Diffusion Guidance Through Riemannian Gradient Descent

arXiv:2606.28417v1 Announce Type: new Abstract: Recently, diffusion models have been widely adopted in generative modeling and have served as foundational models for many image generation tasks. To control the generation without costly re-training or fine-tuning, many works seek inference-time guidance methods to steer the latent via a differentiable objective at inference time. However, these methods cannot effectively preserve the original Gaussian distribution because they introduce distribut...

30.06.2026
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