arXiv AI
Jul 16
Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation
arXiv:2607. 13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.
How far can we go with ImageNet for Text-to-Image generation?
The paper argues that large-scale text‑to‑image models can be trained effectively on ImageNet, provided the dataset is enriched with carefully crafted text and image augmentations. Using this approach, the authors match the performance of state‑of‑the‑art models such as FLUX, surpassing SD3 on GenEval by +5 points and SDXL on DPGBench by +12, while employing only 1/1000th of the training images and significantly fewer parameters. The method requires just 500 hours of H100 GPU time, making it a more reproducible and accessible alternative to massive web‑scraped datasets.
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arXiv Computer Vision
4d ago
Unified Text-Image Generation with Weakness-Targeted Post-Training
The paper introduces a post‑training approach that enables a single inference process to transition from text reasoning to image synthesis, eliminating the need for explicit modality switching. Using the 14B BAGEL model, the authors demonstrate that targeted post‑training data and reward‑weighted training improve multimodal image generation across four independent T2I benchmarks. The study highlights the benefits of joint text‑image generation and strategic data selection for enhancing T2I performance.
arXiv AI
Jul 21
Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget
arXiv:2607. 13125v2 Announce Type: replace-cross Abstract: We introduce Boogu-Image-0.
Hugging Face Trending Papers
Aug 17
Nexus: Structured Synergy for Efficient Text-to-Image Generation using Rectified Flow Model
Diffusion and flow matching models have made significant progress in text-to-image generation, yet high computation, quadratic complexity, and large memory footprint hinder high-resolution synthesis and edge deployment. We propose Nexus, which integrates sparse architecture, linear complexity, and low-bit quantization.
arXiv AI
Jun 9
BLM-SGAN: Bidirectional Language Modeling for Semantic-Spatial Text-to-Image Generation
arXiv:2606. 08847v1 Announce Type: cross Abstract: Despite the success of image generation from text descriptions, it still faces challenges that are difficult to overcome in domains such as natural language processing (NLP) and computer vision (CV).
arXiv AI
Sep 4
LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes
LLaDA-Image is a unified framework that couples a 6B Diffusion Transformer (DiT) trained from scratch with a frozen vision‑language module based on the LLaDA2.0‑Mini diffusion language model. The approach first builds a strong visual generative prior through image‑only pre‑training and mid‑training, then fine‑tunes with a 220M‑sample generation pipeline that includes 98 real images. The resulting model produces highly photorealistic images that accurately follow fine‑grained editing instructions, and a distilled version, LLaDA‑Image‑Turbo, enables fast inference in 2–4 sampling steps. On Qwen‑Image‑Bench, LLaDA‑Image sets new state‑of‑the‑art scores for open‑source models in both English and Chinese tracks, and the authors release weights, code, and detailed recipes to support further research.
arXiv Machine Learning
Aug 27
Continuous Adversarial Flow Models
The paper introduces continuous adversarial flow models, a continuous-time flow framework trained with an adversarial objective that replaces the fixed mean-squared-error criterion of flow matching. By incorporating a learned discriminator, the method guides training toward a different generalized distribution, yielding samples more closely aligned with the target data distribution. Applied as a post‑training step, it markedly improves ImageNet 256px generation metrics—reducing the guidance‑free FID of latent‑space SiT from 8.26 to 3.63 and of pixel‑space JiT from 7.17 to 3.57—and also enhances guided generation and text‑to‑image benchmarks.
arXiv Computer Vision
Aug 24
Exploring the Performance Frontier of Compact Unified Image Generation Models
Swift-Image is a compact unified model that performs text-to-image generation, single-image editing, and multi-image editing using a 6B parameter DiT architecture. It employs a progressive training pipeline, parallel expert reinforcement learning, and multi-teacher distillation to balance diverse objectives, while a Prompt Enhancer decouples high-level reasoning from pixel-level rendering. After training, structural pruning and few-step distillation produce efficient 3B and accelerated variants that maintain near‑lossless performance and improve editing efficiency.
arXiv Machine Learning
Jun 10
Cost-Aware Routing for Efficient Text-To-Image Generation
arXiv:2506. 14753v3 Announce Type: replace-cross Abstract: Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process.
arXiv AI
Jul 10
TMI: Text-to-Image Meets Image-to-Image for Complementary Data Synthesis to Boost Long-Tailed Instance Segmentation
arXiv:2607. 08201v1 Announce Type: cross Abstract: Large-vocabulary instance segmentation is constrained by long-tailed category distributions and fine-grained inter-class ambiguity.
arXiv AI
Aug 18
OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing
arXiv:2509. 24900v2 Announce Type: replace-cross Abstract: The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data.
arXiv Computer Vision
Aug 21
Swift-Image: Exploring the Performance Frontier of Compact Unified Image Generation Models
arXiv:2608. 20334v1 Announce Type: new Abstract: We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing.