Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models
Arithmetic circuits, such as adders and multipliers, are fundamental components of digital systems, directly impacting the performance, power efficiency, and area footprint. However, optimizing these circuits remains cha…
Conditional Image GenerationNoise Conditional Variational Score Distillation
We propose Noise Conditional Variational Score Distillation (NCVSD), a novel method for distilling pretrained diffusion models into generative denoisers. We achieve this by revealing that the unconditional score function…
Conditional Image GenerationDenoisingImage GenerationForward-only Diffusion Probabilistic Models
This work presents a forward-only diffusion (FoD) approach for generative modelling. In contrast to traditional diffusion models that rely on a coupled forward-backward diffusion scheme, FoD directly learns data generati…
Conditional Image GenerationImage DehazingImage GenerationImage Restoration+2Context-Aware Autoregressive Models for Multi-Conditional Image Generation
Autoregressive transformers have recently shown impressive image generation quality and efficiency on par with state-of-the-art diffusion models. Unlike diffusion architectures, autoregressive models can naturally incorp…
Conditional Image GenerationImage GenerationEfficient Flow Matching using Latent Variables
Flow matching models have shown great potential in image generation tasks among probabilistic generative models. Building upon the ideas of continuous normalizing flows, flow matching models generalize the transport path…
Conditional Image GenerationImage GenerationVistaDepth: Frequency Modulation With Bias Reweighting For Enhanced Long-Range Depth Estimation
Monocular depth estimation (MDE) aims to predict per-pixel depth values from a single RGB image. Recent advancements have positioned diffusion models as effective MDE tools by framing the challenge as a conditional image…
Conditional Image GenerationDepth EstimationImage GenerationMonocular Depth EstimationPRISM: A Unified Framework for Photorealistic Reconstruction and Intrinsic Scene Modeling
We present PRISM, a unified framework that enables multiple image generation and editing tasks in a single foundational model. Starting from a pre-trained text-to-image diffusion model, PRISM proposes an effective fine-t…
Conditional Image GenerationImage GenerationIntrinsic Image DecompositionText to Image Generation+1PixelFlow: Pixel-Space Generative Models with Flow
We present PixelFlow, a family of image generation models that operate directly in the raw pixel space, in contrast to the predominant latent-space models. This approach simplifies the image generation process by elimina…
Conditional Image GenerationImage GenerationA Unified Agentic Framework for Evaluating Conditional Image Generation
Conditional image generation has gained significant attention for its ability to personalize content. However, the field faces challenges in developing task-agnostic, reliable, and explainable evaluation metrics. This pa…
Conditional Image GenerationImage GenerationA Hybrid Wavelet-Fourier Method for Next-Generation Conditional Diffusion Models
We present a novel generative modeling framework,Wavelet-Fourier-Diffusion, which adapts the diffusion paradigm to hybrid frequency representations in order to synthesize high-quality, high-fidelity images with improved …
Conditional Image GenerationImage GenerationTexture SynthesisLess-to-More Generalization: Unlocking More Controllability by In-Context Generation
Although subject-driven generation has been extensively explored in image generation due to its wide applications, it still has challenges in data scalability and subject expansibility. For the first challenge, moving fr…
Conditional Image GenerationImage GenerationPersonalized Image GenerationText-to-Image GenerationEfficient Multi-Instance Generation with Janus-Pro-Dirven Prompt Parsing
Recent advances in text-guided diffusion models have revolutionized conditional image generation, yet they struggle to synthesize complex scenes with multiple objects due to imprecise spatial grounding and limited scalab…
Conditional Image GenerationImage GenerationLayout GenerationD2C: Unlocking the Potential of Continuous Autoregressive Image Generation with Discrete Tokens
In the domain of image generation, latent-based generative models occupy a dominant status; however, these models rely heavily on image tokenizer. To meet modeling requirements, autoregressive models possessing the chara…
Conditional Image GenerationImage GenerationDi$\mathtt{[M]}$O: Distilling Masked Diffusion Models into One-step Generator
Masked Diffusion Models (MDMs) have emerged as a powerful generative modeling technique. Despite their remarkable results, they typically suffer from slow inference with several steps. In this paper, we propose Di$\matht…
Conditional Image GenerationImage GenerationText to Image GenerationText-to-Image GenerationDiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving
End-to-end autonomous driving (E2E-AD) has rapidly emerged as a promising approach toward achieving full autonomy. However, existing E2E-AD systems typically adopt a traditional multi-task framework, addressing perceptio…
Autonomous DrivingBench2DriveConditional Image GenerationImage GenerationAutoregressive Image Generation with Randomized Parallel Decoding
We introduce ARPG, a novel visual autoregressive model that enables randomized parallel generation, addressing the inherent limitations of conventional raster-order approaches, which hinder inference efficiency and zero-…
Conditional Image GenerationImage GenerationImage InpaintingZero-shot GeneralizationActive Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets
Recent advances in conditional image generation from diffusion models have shown great potential in achieving impressive image quality while preserving the constraints introduced by the user. In particular, ControlNet en…
Active LearningConditional Image GenerationImage GenerationSemantic Segmentation+1OminiControl2: Efficient Conditioning for Diffusion Transformers
Fine-grained control of text-to-image diffusion transformer models (DiT) remains a critical challenge for practical deployment. While recent advances such as OminiControl and others have enabled a controllable generation…
Conditional Image GenerationDenoisingImage GenerationDICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks
Our primary goal here is to create a good, generalist perception model that can tackle multiple tasks, within limits on computational resources and training data. To achieve this, we resort to text-to-image diffusion mod…
Conditional Image GenerationImage GenerationSemantic SegmentationDC-ControlNet: Decoupling Inter- and Intra-Element Conditions in Image Generation with Diffusion Models
In this paper, we introduce DC (Decouple)-ControlNet, a highly flexible and precisely controllable framework for multi-condition image generation. The core idea behind DC-ControlNet is to decouple control conditions, tra…
Conditional Image GenerationImage Generation