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.
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.
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arXiv AI
Jun 3
Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models
arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.
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 AI
Jul 8
Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation
arXiv:2607. 06481v1 Announce Type: cross Abstract: We present PACR-Video, a parameter-efficient framework for multi-shot long video extrapolation that preserves recurring entities, scene structure, visual style, and causal progression without full generator fine-tuning.
arXiv AI
Jul 10
Reinforcing the Generation Order of Multimodal Masked Diffusion Models
arXiv:2607. 08056v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks.
arXiv AI
Jun 26
Semantic Generative Tuning for Unified Multimodal Models
arXiv:2605. 18714v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture.
arXiv AI
Jun 10
Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling
arXiv:2512. 12675v3 Announce Type: replace-cross Abstract: Subject-driven image generation has advanced from single- to multi-subject composition, while neglecting distinction, the ability to distinguish and generate the correct subject when inputs contain multiple candidates.
Hugging Face Trending Papers
Jun 22
Keep The Essentials: Efficient Reference Conditioned Generation via Token Dropping
Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis. However, these models are computationally expensive in runtime, and their cost scales severely with the number of input references.
Hugging Face Trending Papers
Jul 9
Reinforcing the Generation Order of Multimodal Masked Diffusion Models
Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that adaptive token generation ordering can significantly improve performance in mathematical reasoning and code synthesis applications.
arXiv AI
Jul 3
SEPS: Semantic-enhanced Patch Slimming Framework for fine-grained cross-modal alignment
arXiv:2511. 01390v2 Announce Type: replace-cross Abstract: Fine-grained cross-modal alignment aims to establish precise local correspondences between vision and language, forming a cornerstone for visual question answering and related multimodal applications.
arXiv AI
Jun 24
IV-CoT: Implicit Visual Chain-of-Thought for Structure-Aware Text-to-Image Generation
arXiv:2606. 24849v1 Announce Type: cross Abstract: Unified multi-modal large language models (MLLMs) have achieved strong text-to-image generation quality, but still struggle with structure-aware prompt following, where object counts, spatial relations, attribute bindings, and coarse layouts must be preserved.
arXiv AI
Jun 9
ViMax: Agentic Video Generation
arXiv:2606. 07649v1 Announce Type: cross Abstract: Long-form video generation requires systematic narrative planning and visual consistency that current short-clip methods cannot provide.