Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
Diffusion Models without Classifier-free Guidance
This paper presents Model-guidance (MG), a novel objective for training diffusion model that addresses and removes of the commonly used Classifier-free guidance (CFG). Our innovative approach transcends the standard mode…
Conditional Image GenerationImage GenerationOutsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
Any well-behaved generative model over a variable $\mathbf{x}$ can be expressed as a deterministic transformation of an exogenous ('outsourced') Gaussian noise variable $\mathbf{z}$: $\mathbf{x}=f_\theta(\mathbf{z})$. In…
Conditional Image GenerationImage GenerationG2PDiffusion: Genotype-to-Phenotype Prediction with Diffusion Models
Discovering the genotype-phenotype relationship is crucial for genetic engineering, which will facilitate advances in fields such as crop breeding, conservation biology, and personalized medicine. Current research usuall…
Conditional Image GenerationImage GenerationPredictionAugmented Conditioning Is Enough For Effective Training Image Generation
Image generation abilities of text-to-image diffusion models have significantly advanced, yielding highly photo-realistic images from descriptive text and increasing the viability of leveraging synthetic images to train …
Conditional Image GenerationDescriptiveDiversityFew-Shot Image Classification+3Poisson Flow Joint Model for Multiphase contrast-enhanced CT
In clinical practice, multiphase contrast-enhanced CT (MCCT) is important for physiological and pathological imaging with contrast injection, which undergoes non-contrast, venous, and delayed phases. Inevitably, the accu…
Conditional Image GenerationImage GenerationSSIMCompressed Image Generation with Denoising Diffusion Codebook Models
We present a novel generative approach based on Denoising Diffusion Models (DDMs), which produces high-quality image samples along with their losslessly compressed bit-stream representations. This is obtained by replacin…
Conditional Image GenerationDenoisingDiversityImage Compression+2EditAR: Unified Conditional Generation with Autoregressive Models
Recent progress in controllable image generation and editing is largely driven by diffusion-based methods. Although diffusion models perform exceptionally well in specific tasks with tailored designs, establishing a unif…
Conditional Image GenerationImage GenerationImage SegmentationSemantic SegmentationNoiseCtrl: A Sampling-Algorithm-Agnostic Conditional Generation Method for Diffusion Models
In training-free conditional generative tasks, diffusion models utilize differentiable loss functions to steer the generative reverse process, necessitating modifications to sampling algorithms like DDPM and DDIM. Ho…
Conditional Image GenerationImage GenerationGLASS: Guided Latent Slot Diffusion for Object-Centric Learning
Object-centric learning aims to decompose an input image into a set of meaningful object files (slots). These latent object representations enable a variety of downstream tasks. Yet, object-centric learning struggles…
Conditional Image GenerationImage GenerationObjectObject DiscoveryAn Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models
Diffusion bridge models have demonstrated promising performance in conditional image generation tasks, such as image restoration and translation, by initializing the generative process from corrupted images instead of pu…
Conditional Image GenerationImage GenerationImage RestorationSuper-Resolution+1Self-control: A Better Conditional Mechanism for Masked Autoregressive Model
Autoregressive conditional image generation algorithms are capable of generating photorealistic images that are consistent with given textual or image conditions, and have great potential for a wide range of applications…
Conditional Image GenerationImage GenerationQuantizationEfficient Generative Modeling with Residual Vector Quantization-Based Tokens
We explore the use of Residual Vector Quantization (RVQ) for high-fidelity generation in vector-quantized generative models. This quantization technique maintains higher data fidelity by employing more in-depth tokens. H…
Conditional Image GenerationImage GenerationQuantizationSpeech Synthesis+4Generative Zoo
The model-based estimation of 3D animal pose and shape from images enables computational modeling of animal behavior. Training models for this purpose requires large amounts of labeled image data with precise pose and sh…
Conditional Image GenerationImage GenerationSynthetic Data 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 GenerationIQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative Models
Diffusion-based models have recently transformed conditional image generation, achieving unprecedented fidelity in generating photorealistic and semantically accurate images. However, consistently generating high-quality…
Adversarial RobustnessConditional Image GenerationImage GenerationImage Quality Assessment+1AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer
Quantitative analysis of animal behavior and biomechanics requires accurate animal pose and shape estimation across species, and is important for animal welfare and biological research. However, the small network capacit…
Conditional Image GenerationContrastive LearningImage GenerationHigh-Resolution Image Synthesis via Next-Token Prediction
Denoising with a Joint-Embedding Predictive Architecture (D-JEPA), an autoregressive model, has demonstrated outstanding performance in class-conditional image generation. However, the application of next-token predictio…
Conditional Image GenerationDenoisingImage GenerationPrediction+2Constant Rate Schedule: Constant-Rate Distributional Change for Efficient Training and Sampling in Diffusion Models
We propose a noise schedule that ensures a constant rate of change in the probability distribution of diffused data throughout the diffusion process. To obtain this noise schedule, we measure the rate of change in the pr…
Conditional Image GenerationImage GenerationAdaptively Controllable Diffusion Model for Efficient Conditional Image Generation
With the development of artificial intelligence, more and more attention has been put onto generative models, which represent the creativity, a very important aspect of intelligence. In recent years, diffusion models hav…
Conditional Image GenerationImage 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 Generation