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.
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.
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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
Jul 1
DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation
arXiv:2603. 08090v3 Announce Type: replace-cross Abstract: Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depicting target subjects according to user instructions.
arXiv AI
Jul 9
Tree-of-Thoughts Reasoning for Text-to-Image In-Context Learning
arXiv:2607. 07117v1 Announce Type: cross Abstract: In text-to-image in-context learning (T2I-ICL), a model has to infer a latent compositional pattern from fewshot demonstrations for generating a query image.
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.
arXiv AI
Jun 26
LCG: Long-Context Consistent Image Generation with Sparse Relational Attention
arXiv:2606. 26171v1 Announce Type: cross Abstract: Recent image generation models achieve impressive quality in single-image synthesis, but often fail to maintain consistency across sequential outputs, as required in comics, storyboards, and visual narratives.
arXiv Machine Learning
Jul 2
AdaBoosting Text Prompts for Vision-Language Models
arXiv:2607. 00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts.
arXiv AI
Jun 9
IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment
arXiv:2606. 08016v1 Announce Type: cross Abstract: Current image editing software often hinges on fixed filters or expert tuning, leaving a gap between amateur users' intent and outcomes.
arXiv AI
Jun 2
You Don't Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models
arXiv:2603. 00133v2 Announce Type: replace-cross Abstract: Generative models have been shown to "memorize" certain training data, leading to verbatim or near-verbatim generating images, which may cause privacy concerns or copyright infringement.
arXiv AI
Jul 31
Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives
arXiv:2607. 26735v1 Announce Type: cross Abstract: Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without extensive prompt engineering.
arXiv AI
Aug 11
Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
arXiv:2608. 09666v1 Announce Type: new Abstract: Recent advances in visual generative models have enabled high-quality image and video generation, but evaluating these models often demands sampling hundreds or thousands of images or videos, which is computationally expensive.
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).