Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
A Plug-in Interpretation of Conditioning in Score-Based Diffusion Models
We propose a conditioning mechanism for diffusion models based on multi-speed joint diffusion of the target and the condition. The mechanism learns an unconditional joint score network and enforces conditioning at infere…
Conditional Image GenerationEnergy-Guided Flow Matching
Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flow matching interpolates noise toward a f…
Conditional Image GenerationText-to-Image GenerationInference-Time Scaling of Diffusion Models via Progressive Seed Pruning
Diffusion and flow-matching models dominate conditional image generation, yet inference-time scaling for these models is far less developed than for autoregressive language models. Because final quality is highly sensiti…
Conditional Image GenerationDICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models
Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and…
Conditional Image GenerationImage Super-ResolutionImage DeblurringStyle TransferShift-and-Sum Quantization for Visual Autoregressive Models
Post-training quantization (PTQ) enables efficient deployment of deep networks using a small set of data. Its application to visual autoregressive models (VAR), however, remains relatively unexplored. We identify two key…
Conditional Image GenerationDivide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models
The abundance of pre-trained diffusion models provides an opportunity for composition. Combining several models, however, runs the risk of one model dominating or models disagreeing with each other. Here, we propose Divi…
Conditional Image GenerationCompositional Generative Modeling from Decentralized Data
Learning the compositional nature of the physical world requires joint observation of interacting factors. However, because practical data is often decentralized, these factors are fragmented across isolated silos. Exist…
Conditional Image GenerationFederated LearningCoFi-UCGen: Coarse-to-Fine Unsupervised Conditional Generation without Label Priors
Unsupervised conditional image generation (UCGen) aims to control generation without relying on manually annotated labels, yet remains challenging due to unstructured semantic representations across granularities. To add…
Conditional Image GenerationVPG: Visual Prefix Guidance for Autoregressive Image and Video Generation
Autoregressive image and video generators are trained with teacher-forced histories but must sample from their own generated prefixes at inference time, making them vulnerable to exposure bias and prefix drift. Existing …
Conditional Image GenerationText-to-Video GenerationText-to-Image GenerationDiffusion Domain Expansion: Learning to Coordinate Pre-trained Diffusion Models
In this paper, we propose Diffusion Domain Expansion (DDE), a method that efficiently extends pre-trained diffusion models to generate larger objects and handle more complex conditioning beyond their original capabilitie…
Conditional Image GenerationAtteConDA: Attention-Based Conflict Suppression in Multi-Condition Diffusion Models and Synthetic Data Augmentation
Recent conditional image generation methods can improve controllability by generating images that are faithful to conditions such as sketches, human poses, segmentation maps, and depth. By applying these techniques to im…
Conditional Image GenerationSemantic SegmentationImage AugmentationData AugmentationColorful-Noise: Training-Free Low-Frequency Noise Manipulation for Color-Based Conditional Image Generation
Text-to-image diffusion models generate images by gradually converting white Gaussian noise into a natural image. White Gaussian noise is well suited for producing diverse outputs from a single text prompt due to its abs…
Conditional Image GenerationTokenLight: Precise Lighting Control in Images using Attribute Tokens
This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We formulate relighting as a conditional image generation task and intr…
Conditional Image GenerationContinuous ControlInverse RenderingImage RelightingGenerative Refinement Networks for Visual Synthesis
While diffusion models dominate the field of visual generation, they are computationally inefficient, applying a uniform computational effort regardless of different complexity. In contrast, autoregressive (AR) models ar…
Conditional Image GenerationText-to-Video GenerationImage ReconstructionPHAC: Promptable Human Amodal Completion
Conditional image generation methods are increasingly used in human-centric applications, yet existing human amodal completion (HAC) models offer users limited control over the completed content. Given an occluded person…
Conditional Image GenerationRectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma
Brain tumors result in 20 years of lost life on average. Standard therapies induce complex structural changes in the brain that are monitored through MRI. Recent developments in artificial intelligence (AI) enable condit…
Conditional Image GenerationDMAligner: Enhancing Image Alignment via Diffusion Model Based View Synthesis
Image alignment is a fundamental task in computer vision with broad applications. Existing methods predominantly employ optical flow-based image warping. However, this technique is susceptible to common challenges such a…
Conditional Image GenerationCoLoGen: Progressive Learning of Concept-Localization Duality for Unified Image Generation
Unified conditional image generation remains difficult because different tasks depend on fundamentally different internal representations. Some require conceptual understanding for semantic synthesis, while others rely o…
Conditional Image GenerationEfficient Text-Guided Convolutional Adapter for the Diffusion Model
We introduce the Nexus Adapters, novel text-guided efficient adapters to the diffusion-based framework for the Structure Preserving Conditional Generation (SPCG). Recently, structure-preserving methods have achieved prom…
Conditional Image GenerationAmbient Dataloops: Generative Models for Dataset Refinement
We propose Ambient Dataloops, an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying data distribution. Modern datasets contain samples of highly varying quality, a…
Conditional Image GenerationProtein Design