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.
Appearance Pointers -- Multimodal Region Control of Diffusion Transformers
arXiv:2607. 19344v1 Announce Type: cross Abstract: Controllable image generation remains challenging for creative professionals, who often require precise regional control over materials, object identities, and spatial arrangements that cannot be reliably achieved through text prompting alone.
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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 4
On-the-fly Repulsion in the Contextual Space for Rich Diversity in Diffusion Transformers
arXiv:2603. 28762v2 Announce Type: replace-cross Abstract: Modern Text-to-Image (T2I) diffusion models have achieved remarkable semantic alignment, yet they often suffer from a significant lack of variety, converging on a narrow set of visual solutions for any given prompt.
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.
Hugging Face Trending Papers
Jul 23
MagicMakeup: A Region-Controllable Diffusion Transformer for High-Fidelity Makeup-Transfer
Makeup-transfer applies the reference makeup to the source face while preserving the source identity. Despite advances in full-face editing by diffusion-based methods, strong regional controllability, makeup fidelity, and identity preservation remain challenging.
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 15
HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing
arXiv:2606. 13898v1 Announce Type: cross Abstract: Creative image editing tools, such as Photoshop's Remove or Generative Fill buttons, are central to everyday customer use and account for a major share of traffic in Photoshop and Lightroom.
Hugging Face Trending Papers
Jun 3
MetaPoint: Unlocking Precise Spatial Control in Agentic Visual Generation
Generative visual models fundamentally struggle with precise spatial control. This arises from a core disconnect: models can process textual descriptions of space but cannot directly map numerical coordinates onto the 2D image canvas.
arXiv Machine Learning
Aug 3
CompoSE: Compositional Synthesis and Editing of 3D Shapes via Part-Aware Control
arXiv:2605. 19350v2 Announce Type: replace-cross Abstract: Creating and editing high-quality 3D content remains a central challenge in computer graphics.
Hugging Face Trending Papers
Jul 7
High-Resolution Artwork Outpainting with Global Blueprint Guidance and Layout Control
Image outpainting extends an image beyond its original borders, requiring seamless style integration and globally coherent scene completion. Building on the success of diffusion models, recent methods have achieved substantial improvements in visual quality.
Hugging Face Trending Papers
Jul 7
From RGB Generation to Dense Field Readout: Pixel-Space Dense Prediction with Text-to-Image Models
Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and geometric priors. Existing generative and editing approaches reuse these priors by casting dense prediction as target generation: annotations such as depth, normals, alpha mattes, masks, and heatmaps are encoded into an RGB-trained VAE latent space and decoded back as image-like targets.
arXiv AI
Jul 8
Few Channels Draw The Whole Picture: Revealing Massive Activations in Diffusion Transformers
arXiv:2605. 13974v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) and related flow-based architectures are now among the strongest text-to-image generators, yet the internal mechanisms through which prompts shape image semantics remain poorly understood.