Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model
In this paper, we rethink the low-light image enhancement task and propose a physically explainable and generative diffusion model for low-light image enhancement, termed as Diff-Retinex. We aim to integrate the advantag…
Conditional Image GenerationImage EnhancementImage GenerationLow-Light Image EnhancementSoft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data
Label-noise or curated unlabeled data is used to compensate for the assumption of clean labeled data in training the conditional generative adversarial network; however, satisfying such an extended assumption is occasion…
Conditional Image GenerationGenerative Adversarial NetworkImage GenerationPractical and Asymptotically Exact Conditional Sampling in Diffusion Models
Diffusion models have been successful on a range of conditional generation tasks including molecular design and text-to-image generation. However, these achievements have primarily depended on task-specific conditional t…
Computational EfficiencyConditional Image GenerationImage GenerationImage Inpainting+3AniFaceDrawing: Anime Portrait Exploration during Your Sketching
In this paper, we focus on how artificial intelligence (AI) can be used to assist users in the creation of anime portraits, that is, converting rough sketches into anime portraits during their sketching process. The inpu…
Conditional Image GenerationDisentanglementImage GenerationSyncDiffusion: Coherent Montage via Synchronized Joint Diffusions
The remarkable capabilities of pretrained image diffusion models have been utilized not only for generating fixed-size images but also for creating panoramas. However, naive stitching of multiple images often results in …
Conditional Image GenerationDenoisingImage GenerationCocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation
Text-conditional diffusion models are able to generate high-fidelity images with diverse contents. However, linguistic representations frequently exhibit ambiguous descriptions of the envisioned objective imagery, requir…
Conditional Image GenerationImage GenerationDiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models
In this study, we aim to extend the capabilities of diffusion-based text-to-image (T2I) generation models by incorporating diverse modalities beyond textual description, such as sketch, box, color palette, and style embe…
Conditional Image GenerationImage Generationmultimodal generationText-to-Image GenerationDuDGAN: Improving Class-Conditional GANs via Dual-Diffusion
Class-conditional image generation using generative adversarial networks (GANs) has been investigated through various techniques; however, it continues to face challenges such as mode collapse, training instability, and …
Conditional Image GenerationImage GenerationLaCon: Late-Constraint Diffusion for Steerable Guided Image Synthesis
Diffusion models have demonstrated impressive abilities in generating photo-realistic and creative images. To offer more controllability for the generation process, existing studies, termed as early-constraint methods in…
Conditional Image GenerationConditional Text-to-Image SynthesisImage GenerationText-to-Image GenerationSparseGNV: Generating Novel Views of Indoor Scenes with Sparse Input Views
We study to generate novel views of indoor scenes given sparse input views. The challenge is to achieve both photorealism and view consistency. We present SparseGNV: a learning framework that incorporates 3D structures a…
Conditional Image GenerationImage GenerationNoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs
StyleGANs are at the forefront of controllable image generation as they produce a latent space that is semantically disentangled, making it suitable for image editing and manipulation. However, the performance of StyleGA…
Conditional Image GenerationDiversityImage GenerationTrade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability
Recent advancements in diffusion models have significantly impacted the trajectory of generative machine learning research, with many adopting the strategy of fine-tuning pre-trained models using domain-specific text-to-…
Conditional Image GenerationImage GenerationObject LocalizationPhrase GroundingPolynomial Implicit Neural Representations For Large Diverse Datasets
Implicit neural representations (INR) have gained significant popularity for signal and image representation for many end-tasks, such as superresolution, 3D modeling, and more. Most INR architectures rely on sinusoidal p…
Conditional Image GenerationImage GenerationUnified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation
Language-guided image generation has achieved great success nowadays by using diffusion models. However, texts can be less detailed to describe highly-specific subjects such as a particular dog or a certain car, which ma…
Conditional Image GenerationImage GenerationText to Image GenerationText-to-Image GenerationUltra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
Simulating high-resolution detector responses is a computationally intensive process that has long been challenging in Particle Physics. Despite the ability of generative models to streamline it, full ultra-high-granular…
Conditional Image GenerationDensity EstimationInductive BiasMetric Learning+2PE-GAN: Prior Embedding GAN for PXD images at Belle II
The pixel vertex detector (PXD) is an essential part of the Belle II detector recording particle positions. Data from the PXD and other sensors allow us to reconstruct particle tracks and decay vertices. The effect of ba…
Conditional Image GenerationContrastive LearningCollage Diffusion
We seek to give users precise control over diffusion-based image generation by modeling complex scenes as sequences of layers, which define the desired spatial arrangement and visual attributes of objects in the scene. C…
Conditional Image GenerationImage GenerationImage HarmonizationCDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resolution (SR) have shown that iteratively r…
Conditional Image GenerationDenoisingImage GenerationImage Super-Resolution+2MaskSketch: Unpaired Structure-guided Masked Image Generation
Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. However, the majority of these methods allow conditioning only on labels or text prompts, which limits their level…
Conditional Image GenerationDiversityImage GenerationImage-to-Image Translation+2Simple diffusion: End-to-end diffusion for high resolution images
Currently, applying diffusion models in pixel space of high resolution images is difficult. Instead, existing approaches focus on diffusion in lower dimensional spaces (latent diffusion), or have multiple super-resolutio…
Conditional Image GenerationDenoisingImage GenerationSuper-Resolution+2