…Generative Image Parsing of Raster Graphic Designs into Editable Layers (2026) simpleposter: a simple baseline for product poster generation (2026) Multimodal Large Language Models for Multi-Subject In-Context Image Generation (2026…
…Our experimental results demonstrate that the gains from target-aware representation tuning generalize across both text and image modalities, several tabular learners, encoder scales, and embedding dimensions. MulTaBench constitutes the largest image…
…A Vision-Only Generative Model for Image Super-Resolution (2026) Allo{SR}$^2$: Rectifying One-Step Super-Resolution to Stay Real via Allomorphic Generative Flows (2026) BurstGP: Enhancing Raw Burst Image Super…
Papers arxiv:2605.12305 Images in Sentences: Scaling Interleaved Instructions for Unified Visual Generation Published on May 12 Submitted by taesiri on May 13 Authors: , , , , Abstract INSET is a unified multimodal model…
This is really helpful! The way Claude connects with Hugging Face to generate images makes things so much easier for users. I think if this feature is also embedded on websites , it…
…AI-generated summary Deep generative models have advanced rapidly across text and vision, motivating unified multimodal systems that can understand, reason over, and generate interleaved text-image sequences. Most existing approaches combine…
…Specifically, we first introduce a scalable, annotation-free data generation pipeline that synthesizes 4D reasoning data from raw videos. We then propose Dynamic-Imagery Fine-Tuning (DIFT), which jointly supervises textual tokens…
…We show that the pixel AsymFlow model finetuned from FLUX.2 klein 9B establishes a new state of the art for pixel-space text-to-image generation , beating its latent base on…
…A Fast Parallel Transform-based Codec for Efficient Asymmetric Signal Compression (2026) Differentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression (2026) CoD-Lite: Real-Time Diffusion-Based Generative Image…
…AI-generated summary We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fidelity and diffusability . To address the reconstruction…