Papers Text-to-Image Generation
“Text-to-Image Generation” 태그가 달린 논문 1,559편 · 필터 해제
DC-SAE: Deep Compression Semantic Autoencoder for Faster Diffusion Convergence
High-compression tokenizers are essential for scaling latent image generative models. However, aggressive compression creates a fundamental tradeoff between reconstruction fidelity and generation efficiency: high compres…
Text-to-Image GenerationImage ReconstructionHiRAE: Hierarchical Representation Autoencoding with Residual Budgets
Pretrained visual representations support image generation, but may not fully preserve the fine-grained details needed for faithful reconstruction. Meanwhile, intermediate encoder layers contain complementary visual deta…
Text-to-Image GenerationImproved Distributional Diffusion Models
Distributional Diffusion Models (DDMs) replace the standard mean-prediction denoiser with a distributional denoiser trained via a scoring rule objective, learning a stochastic approximation to p(x_1 mid x_t) rather than …
Text-to-Image GenerationUnifying Distributional Training for One-Step Visual Generation
Distributional training provides collective supervision for one-step visual generation by matching real and generated features in frozen representation spaces. We introduce a unified theoretical framework that separates …
Text-to-Image GenerationOn the Diffusibility of High-Dimensional Latents
Representation Autoencoders (RAEs) enable diffusion models to operate in the feature spaces of pretrained visual encoders. However, many off-the-shelf encoders are not optimized for faithful reconstruction, discarding fi…
Text-to-Image GenerationImage ReconstructionEfficient Text-to-Image Generation: An Adaptive Step Schedule Controller for Diffusion Models
Text-to-image diffusion models often use a fixed number of denoising steps, balancing time costs and image quality. However, the optimal number of steps depends on the complexity of the input text prompt. We propose an a…
Text-to-Image GenerationImportance-Aware Low-Rank Distillation of Diffusion Transformers
Diffusion Transformers (DiTs) have emerged as a dominant architecture for high-quality text-to-image generation, yet their scale poses challenges for efficient deployment. While truncated singular value decomposition (SV…
Text-to-Image GenerationKnowledge DistillationImageEval 2026: Culturally Grounded Arabic Multimodal Evaluation
We present an overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) AynVQA, covering spoken visual question answering and image-grounded hallucination …
Visual Question AnsweringText-to-Image GenerationGenFirst: Generation Before Reconstruction for Stable End-to-End Latent Generative Modeling
Latent generative models typically follow a two-stage pipeline, training a variational autoencoder for reconstruction and then a generative model on the frozen latent space. Since reconstruction-optimized latents are not…
Text-to-Image GenerationRepresentation LearningAbstract4D: A Large-Scale Dataset and Framework for Understanding the Visual Language of Abstract Art
Artificial intelligence can classify artistic styles and synthesize images, but it still lacks a model of the visual language that gives art meaning. Abstract painting minimizes object semantics and foregrounds structura…
Text-to-Image GenerationCross-Modal RetrievalAttribute Token Arithmetic: Disentangled and Continuous Semantic Control for Visual Autoregressive Models
Autoregressive text-to-image generation has recently achieved remarkable progress, offering high-fidelity synthesis via a unified generative framework. However, fine-grained semantic control remains challenging due to th…
Computational EfficiencyText-to-Image GenerationRubricRM: Generative Reward Modeling via Dynamic Rubrics for Image Generation and Editing
Reward models play an essential role in aligning visual generative models, yet most existing visual reward models use a single scalar score or rely on fixed criteria that cannot adapt to different instructions. This limi…
Text-to-Image GenerationImage EditingExploring the Performance Frontier of Compact Unified Image Generation Models
We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing. Our goal is to explore how far a relatively small visual generator can be pushed through system…
Text-to-Image GenerationReinforcement LearningModel CompressionImage EditingOptimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization
Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. While much work has focused on efficient solvers and samplers, comparati…
Text-to-Image GenerationNexus: 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. …
Text-to-Image GenerationDiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization
As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial…
Text-to-Image GenerationConcept Guidance: Precise, Training-Free Latent Control for Text-to-Image Generation
Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specific guidance (e.g., for precisely controll…
Text-to-Image GenerationPrompt EngineeringAdversarial Learning of Classifier-Free Guidance Schedules
Modern text-to-image diffusion models rely on classifier-free guidance (CFG) to achieve high image fidelity and text alignment. However, CFG typically applies a static, global scale across all timesteps, samples, and con…
Text-to-Image GenerationTowards Physics-Faithful Generation of Scientific Diagrams
Text-to-image generation has reached photorealistic quality, yet state-of-the-art systems remain unreliable at producing scientific diagrams, whose value depends not on appearance but on physical faithfulness: correct fo…
Text-to-Image GenerationSemantic Steering for Controllable Generation: Tuning-Free Concept Erasure in Multimodal Diffusion Transformers
Multimodal Diffusion Transformers (MM-DiTs) have demonstrated remarkable text-to-image generation performance, surpassing traditional U-Net-based diffusion models. Nevertheless, their powerful generative capabilities als…
Text-to-Image Generation