Papers Unconditional Image Generation
“Unconditional Image Generation” 태그가 달린 논문 73편 · 필터 해제
DiffUMI: Training-Free Universal Model Inversion via Unconditional Diffusion for Face Recognition
Face recognition technology presents serious privacy risks due to its reliance on sensitive and immutable biometric data. To address these concerns, such systems typically convert raw facial images into embeddings, which…
Face GenerationFace RecognitionImage GenerationPrivacy Preserving+1Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restoration
Recently, pre-trained text-to-image (T2I) models have been extensively adopted for real-world image restoration because of their powerful generative prior. However, controlling these large models for image restoration us…
Image GenerationImage RestorationUnconditional Image GenerationEntropy Rectifying Guidance for Diffusion and Flow Models
Guidance techniques are commonly used in diffusion and flow models to improve image quality and consistency for conditional generative tasks such as class-conditional and text-to-image generation. In particular, classifi…
DiversityImage GenerationText to Image GenerationText-to-Image Generation+1A Unified Framework for Diffusion Bridge Problems: Flow Matching and Schrödinger Matching into One
The bridge problem is to find an SDE (or sometimes an ODE) that bridges two given distributions. The application areas of the bridge problem are enormous, among which the recent generative modeling (e.g., conditional or …
Image GenerationUnconditional Image GenerationConjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis
While representation learning and generative modeling seek to understand visual data, unifying both domains remains unexplored. Recent Unified Self-Supervised Learning (SSL) methods have started to bridge the gap between…
Few-Shot LearningImage GenerationRepresentation LearningSelf-Supervised Learning+2Training-Free Safe Denoisers for Safe Use of Diffusion Models
There is growing concern over the safety of powerful diffusion models (DMs), as they are often misused to produce inappropriate, not-safe-for-work (NSFW) content or generate copyrighted material or data of individuals wh…
Image GenerationNegationUnconditional Image GenerationPQD: Post-training Quantization for Efficient Diffusion Models
Diffusionmodels(DMs)havedemonstratedremarkableachievements in synthesizing images of high fidelity and diversity. However, the extensive computational requirements and slow generative speed of diffusion models have limit…
DiversityImage GenerationQuantizationUnconditional Image GenerationNormalizing Flows are Capable Generative Models
Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention …
Conditional Image GenerationDensity EstimationUnconditional Image GenerationFréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets
Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image g…
Computational EfficiencyImage GenerationImage-to-Image TranslationMedical Image Analysis+2Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques
Diffusion models have significant impact on wide range of generative tasks, especially on image inpainting and restoration. Although the improvements on aiming for decreasing number of function evaluations (NFE), the ite…
Image GenerationImage InpaintingUnconditional Image GenerationScalable, Tokenization-Free Diffusion Model Architectures with Efficient Initial Convolution and Fixed-Size Reusable Structures for On-Device Image Generation
Vision Transformers and U-Net architectures have been widely adopted in the implementation of Diffusion Models. However, each architecture presents specific challenges while realizing them on-device. Vision Transformers …
Conditional Image GenerationImage GenerationNoise EstimationUnconditional Image GenerationAdversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step
Score identity Distillation (SiD) is a data-free method that has achieved SOTA performance in image generation by leveraging only a pretrained diffusion model, without requiring any training data. However, its ultimate p…
Conditional Image GenerationGPUImage GenerationUnconditional Image GenerationStabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typically leverage reconstructive autoencod…
Conditional Image GenerationImage GenerationLinear-Probe ClassificationSelf-Supervised Image Classification+2Edge-preserving noise for diffusion models
Classical generative diffusion models learn an isotropic Gaussian denoising process, treating all spatial regions uniformly, thus neglecting potentially valuable structural information in the data. Inspired by the long-e…
DenoisingImage GenerationUnconditional Image GenerationVariational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling
Energy based models (EBMs) are appealing for their generality and simplicity in data likelihood modeling, but have conventionally been difficult to train due to the unstable and time-consuming implicit MCMC sampling duri…
Image GenerationUnconditional Image GenerationDeep MMD Gradient Flow without adversarial training
We propose a gradient flow procedure for generative modeling by transporting particles from an initial source distribution to a target distribution, where the gradient field on the particles is given by a noise-adaptive …
DenoisingImage GenerationUnconditional Image GenerationGenerative Modelling with High-Order Langevin Dynamics
Diffusion generative modelling (DGM) based on stochastic differential equations (SDEs) with score matching has achieved unprecedented results in data generation. In this paper, we propose a novel fast high-quality genera…
Image GenerationUnconditional Image GenerationLD-Pruner: Efficient Pruning of Latent Diffusion Models using Task-Agnostic Insights
Latent Diffusion Models (LDMs) have emerged as powerful generative models, known for delivering remarkable results under constrained computational resources. However, deploying LDMs on resource-limited devices remains a …
Audio GenerationImage GenerationUnconditional Image GenerationDiffscaler: Enhancing the Generative Prowess of Diffusion Transformers
Recently, diffusion transformers have gained wide attention with its excellent performance in text-to-image and text-to-vidoe models, emphasizing the need for transformers as backbone for diffusion models. Transformer-ba…
Image GenerationUnconditional Image GenerationDiffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models
Transformers have catalyzed advancements in computer vision and natural language processing (NLP) fields. However, substantial computational complexity poses limitations for their application in long-context tasks, such …
Image GenerationUnconditional Image Generation