arXiv AI
Jun 17
Detail++: Training-Free Detail Enhancer for Text-to-Image Diffusion Models
arXiv:2507. 17853v2 Announce Type: replace-cross Abstract: Recent advances in text-to-image (T2I) generation have led to impressive visual results.
MOSS-Image Technical Report
MOSS-Image is an open-weight image generation system that combines a 5B single-stream diffusion transformer with an intention-aware prompt enhancement module to produce high-fidelity, text-rich images with precise layout control. The system supports bilingual long-form text rendering and introduces evaluation benchmarks LongLongTextBench and RealLayoutArena. It achieves state‑of‑the‑art performance on LLTB and ranks highly on RLA, with plans to release the model, training recipe, and evaluation framework for the community.
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arXiv AI
Jun 9
Seeing is Believing: Aligning Prompt Rewriting with Visual Anchors for Text-to-Image Generation
arXiv:2606. 08492v1 Announce Type: cross Abstract: Despite the impressive capabilities of text-to-image (T2I) models, an intent-generation gap often persists due to the brevity and ambiguity of user prompts.
arXiv AI
Oct 9
From Prompting to Composing: A Spatial Canvas Interface for Poster Generation
The paper introduces a Spatial Canvas Interface that lets users directly compose poster generation intent in space using semantic, identity, text, and pixel bindings, along with text specifications for elements and overall appearance. It presents Compo, a model adapted from a pretrained image editing system to interpret these canvas inputs, supporting both manual construction and an agentic mode that auto‑generates a canvas from high‑level requests. A scalable training pipeline and benchmark demonstrate that Compo offers stronger compositional control and visual quality than existing general‑purpose and dedicated poster generation models.
arXiv Computation and Language
Sep 30
POET: Preference Optimization for Enhanced Text-to-Image Generation
arXiv:2510.12041v3 Announce Type: replace Abstract: Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecifie...
arXiv AI
Jun 2
DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?
arXiv:2505. 16915v3 Announce Type: replace-cross Abstract: While recent Text-to-Image (T2I) models show impressive capabilities in synthesizing images from brief descriptions, they struggle with the long, detailed prompts required for professional applications.
arXiv Computer Vision
Oct 16
Text-to-Image Synthesis: A Decade Survey
This survey reviews 490 papers on text‑to‑image synthesis (T2I) over the past decade, covering key model families such as GANs, autoregressive models, and diffusion models. It organizes recent research by problem areas—controllability, personalization, editing, preference alignment, efficiency, safety, and spatial consistency—rather than strictly chronologically. The paper also examines unified multimodal models, datasets, evaluation metrics, and discusses applications, challenges, and future research directions in T2I.
arXiv Computer Vision
Sep 11
SenseNova-U1.5: Towards Native Unified Visual Intelligence
SenseNova-U1.5 is an 8B‑MoT native unified multimodal model that can understand, reason about, and generate visual content without using an encoder or VAE. It improves visual fidelity and text rendering through spatially coherent patch reconstruction, large‑scale training on curated generation and editing data, and native resolutions up to 4K. Post‑training, specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing are optimized and distilled into a multi‑expert framework, yielding advances in image fidelity, complex composition, multi‑reference editing, and instruction following.
arXiv Computer Vision
Sep 3
GlyphAnchor: Enhancing Visual Text Rendering via Position-Anchored Glyph Priors
GlyphAnchor is a new method that improves visual text rendering in image generation and editing models by adding lightweight glyph patch conditions anchored to the target image’s positional encoding. The approach is trained with staged supervised finetuning and text-aware post‑training, and it works with both text‑to‑image and image‑editing diffusion transformers. Experiments on various backbones and the newly introduced InfoTextBench benchmark show that GlyphAnchor consistently enhances text fidelity while maintaining overall image quality, especially for long, complex, or densely arranged text and rare characters.
arXiv AI
Jun 4
Evaluating Reasoning Fidelity in Visual Text Generation
arXiv:2606. 04479v1 Announce Type: cross Abstract: Recent text-to-image (T2I) models can render highly legible and well-structured text within images, enabling applications including document generation and slide generation.
Hugging Face Trending Papers
Jul 7
PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation
Recent text-to-image models such as DALLE-3 excel at following diverse prompts yet remain blind to individual aesthetic preferences. We study personalized image generation, where models must align outputs with a user's implicit visual preferences based on a few historically preferred images and a short prompt.
arXiv Computer Vision
Sep 22
Planning and Rendering in Concert: DeepFusion of Autoregressive Layouts and Diffusion for Visual Text Generation
arXiv:2609.22916v1 Announce Type: new Abstract: Generating text-rich images from prompts requires both textual fidelity and the coherent integration of text into the surrounding image. An explicit la...
arXiv AI
Aug 7
CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation
arXiv:2603. 08652v2 Announce Type: replace Abstract: Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integration of Chain-of-Thought (CoT) reasoning.