Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
InstructMoLE: Instruction-Guided Mixture of Low-rank Experts for Multi-Conditional Image Generation
Parameter-Efficient Fine-Tuning of Diffusion Transformers (DiTs) for diverse, multi-conditional tasks often suffers from task interference when using monolithic adapters like LoRA. The Mixture of Low-rank Experts (MoLE) …
parameter-efficient fine-tuningConditional Image GenerationMixFlow Training: Alleviating Exposure Bias with Slowed Interpolation Mixture
This paper studies the training-testing discrepancy (a.k.a. exposure bias) problem for improving the diffusion models. During training, the input of a prediction network at one training timestep is the corresponding grou…
Conditional Image GenerationText-to-Image GenerationBideDPO: Conditional Image Generation with Simultaneous Text and Condition Alignment
Conditional image generation enhances text-to-image synthesis with structural, spatial, or stylistic priors, but current methods face challenges in handling conflicts between sources. These include 1) input-level conflic…
Conditional Image GenerationImproved Masked Image Generation with Knowledge-Augmented Token Representations
Masked image generation (MIG) has demonstrated remarkable efficiency and high-fidelity images by enabling parallel token prediction. Existing methods typically rely solely on the model itself to learn semantic dependenci…
Conditional Image GenerationSemantic SimilarityKnowledge GraphsVectorSynth: Fine-Grained Satellite Image Synthesis with Structured Semantics
We introduce VectorSynth, a diffusion-based framework for pixel-accurate satellite image synthesis conditioned on polygonal geographic annotations with semantic attributes. Unlike prior text- or layout-conditioned models…
Conditional Image GenerationPC-Diffusion: Aligning Diffusion Models with Human Preferences via Preference Classifier
Diffusion models have achieved remarkable success in conditional image generation, yet their outputs often remain misaligned with human preferences. To address this, recent work has applied Direct Preference Optimization…
Conditional Image GenerationEnhancing Diffusion Model Guidance through Calibration and Regularization
Classifier-guided diffusion models have emerged as a powerful approach for conditional image generation, but they suffer from overconfident predictions during early denoising steps, causing the guidance gradient to vanis…
Conditional Image GenerationClassifier calibrationDiscovering Latent Graphs with GFlowNets for Diverse Conditional Image Generation
Capturing diversity is crucial in conditional and prompt-based image generation, particularly when conditions contain uncertainty that can lead to multiple plausible outputs. To generate diverse images reflecting this di…
Conditional Image GenerationTokenAR: Multiple Subject Generation via Autoregressive Token-level enhancement
Autoregressive Model (AR) has shown remarkable success in conditional image generation. However, these approaches for multiple reference generation struggle with decoupling different reference identities. In this work, w…
Conditional Image GenerationRobust Learning of Diffusion Models with Extremely Noisy Conditions
Conditional diffusion models have the generative controllability by incorporating external conditions. However, their performance significantly degrades with noisy conditions, such as corrupted labels in the image genera…
Conditional Image GenerationRobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
Diffusion Transformers (DiTs) have recently emerged as a powerful backbone for image generation, demonstrating superior scalability and performance over U-Net architectures. However, their practical deployment is hindere…
Conditional Image GenerationREFINE-CONTROL: A Semi-supervised Distillation Method For Conditional Image Generation
Conditional image generation models have achieved remarkable results by leveraging text-based control to generate customized images. However, the high resource demands of these models and the scarcity of well-annotated d…
Conditional Image GenerationLatent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
Generative modeling, representation learning, and classification are three core problems in machine learning (ML), yet their state-of-the-art (SoTA) solutions remain largely disjoint. In this paper, we ask: Can a unified…
Conditional Image GenerationRepresentation LearningCondition Weaving Meets Expert Modulation: Towards Universal and Controllable Image Generation
The image-to-image generation task aims to produce controllable images by leveraging conditional inputs and prompt instructions. However, existing methods often train separate control branches for each type of condition,…
Conditional Image GenerationA-FloPS: Accelerating Diffusion Models via Adaptive Flow Path Sampler
Diffusion models deliver state-of-the-art generative performance across diverse modalities but remain computationally expensive due to their inherently iterative sampling process. Existing training-free acceleration meth…
Conditional Image GenerationNext Visual Granularity Generation
We propose a novel approach to image generation by decomposing an image into a structured sequence, where each element in the sequence shares the same spatial resolution but differs in the number of unique tokens used, c…
Conditional Image GenerationAniMer+: Unified Pose and Shape Estimation Across Mammalia and Aves via Family-Aware Transformer
In the era of foundation models, achieving a unified understanding of different dynamic objects through a single network has the potential to empower stronger spatial intelligence. Moreover, accurate estimation of animal…
Conditional Image GenerationIwin Transformer: Hierarchical Vision Transformer using Interleaved Windows
We introduce Iwin Transformer, a novel position-embedding-free hierarchical vision transformer, which can be fine-tuned directly from low to high resolution, through the collaboration of innovative interleaved window att…
Conditional Image GenerationSemantic SegmentationImage ClassificationAction RecognitionUnsupervised anomaly detection using Bayesian flow networks: application to brain FDG PET in the context of Alzheimer's disease
Unsupervised anomaly detection (UAD) plays a crucial role in neuroimaging for identifying deviations from healthy subject data and thus facilitating the diagnosis of neurological disorders. In this work, we focus on Baye…
Unsupervised Anomaly DetectionConditional Image GenerationBayesian InferenceScale Your Instructions: Enhance the Instruction-Following Fidelity of Unified Image Generation Model by Self-Adaptive Attention Scaling
Recent advancements in unified image generation models, such as OmniGen, have enabled the handling of diverse image generation and editing tasks within a single framework, accepting multimodal, interleaved texts and imag…
Conditional Image GenerationImage Editing