Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation
Neural network architecture design requires making many crucial decisions. The common desiderata is that similar decisions, with little modifications, can be reused in a variety of tasks and applications. To satisfy that…
Conditional Image GenerationImage 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 GenerationBiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities
We introduce BiGR, a novel conditional image generation model using compact binary latent codes for generative training, focusing on enhancing both generation and representation capabilities. BiGR is the first conditiona…
Conditional Image GenerationImage GenerationImage InpaintingText to Image Generation+2Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion
By embedding discrete representations into a continuous latent space, we can leverage continuous-space latent diffusion models to handle generative modeling of discrete data. However, despite their initial success, most …
Conditional Image GenerationImage GenerationMachine TranslationText 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+2Ctrl-U: Robust Conditional Image Generation via Uncertainty-aware Reward Modeling
In this paper, we focus on the task of conditional image generation, where an image is synthesized according to user instructions. The critical challenge underpinning this task is ensuring both the fidelity of the genera…
Conditional Image GenerationImage GenerationChatHouseDiffusion: Prompt-Guided Generation and Editing of Floor Plans
The generation and editing of floor plans are critical in architectural planning, requiring a high degree of flexibility and efficiency. Existing methods demand extensive input information and lack the capability for int…
Conditional Image GenerationImage GenerationEBDM: Exemplar-guided Image Translation with Brownian-bridge Diffusion Models
Exemplar-guided image translation, synthesizing photo-realistic images that conform to both structural control and style exemplars, is attracting attention due to its ability to enhance user control over style manipulati…
Conditional Image GenerationImage GenerationImage-to-Image TranslationPose-Guided Image Generation+2CtrLoRA: An Extensible and Efficient Framework for Controllable Image Generation
Recently, large-scale diffusion models have made impressive progress in text-to-image (T2I) generation. To further equip these T2I models with fine-grained spatial control, approaches like ControlNet introduce an extra n…
Conditional Image GenerationGPUImage GenerationMaskBit: Embedding-free Image Generation via Bit Tokens
Masked transformer models for class-conditional image generation have become a compelling alternative to diffusion models. Typically comprising two stages - an initial VQGAN model for transitioning between latent space a…
Conditional Image GenerationImage GenerationImage ReconstructionFine-Tuning Image-Conditional Diffusion Models is Easier than You Think
Recent work showed that large diffusion models can be reused as highly precise monocular depth estimators by casting depth estimation as an image-conditional image generation task. While the proposed model achieved state…
Conditional Image GenerationDepth EstimationImage GenerationMonocular Depth Estimation+2From Bird's-Eye to Street View: Crafting Diverse and Condition-Aligned Images with Latent Diffusion Model
We explore Bird's-Eye View (BEV) generation, converting a BEV map into its corresponding multi-view street images. Valued for its unified spatial representation aiding multi-sensor fusion, BEV is pivotal for various auto…
Autonomous DrivingConditional Image GenerationImage GenerationSemantic Segmentation+1Enhancing Conditional Image Generation with Explainable Latent Space Manipulation
In the realm of image synthesis, achieving fidelity to a reference image while adhering to conditional prompts remains a significant challenge. This paper proposes a novel approach that integrates a diffusion model with …
Conditional Image GenerationDenoisingImage GenerationCODE: Confident Ordinary Differential Editing
Conditioning image generation facilitates seamless editing and the creation of photorealistic images. However, conditioning on noisy or Out-of-Distribution (OoD) images poses significant challenges, particularly in balan…
Conditional Image GenerationImage GenerationImage RestorationRetinex-Diffusion: On Controlling Illumination Conditions in Diffusion Models via Retinex Theory
This paper introduces a novel approach to illumination manipulation in diffusion models, addressing the gap in conditional image generation with a focus on lighting conditions. We conceptualize the diffusion model as a b…
Conditional Image GenerationImage GenerationGuided Latent Slot Diffusion for Object-Centric Learning
Slot attention aims to decompose an input image into a set of meaningful object files (slots). These latent object representations enable various downstream tasks. Yet, these slots often bind to object parts, not objects…
Conditional Image GenerationDecoderImage GenerationObject+3Scaling Diffusion Transformers to 16 Billion Parameters
In this paper, we present DiT-MoE, a sparse version of the diffusion Transformer, that is scalable and competitive with dense networks while exhibiting highly optimized inference. The DiT-MoE includes two simple designs:…
AttributeConditional Image GenerationDenoisingImage Generation+1HumanRefiner: Benchmarking Abnormal Human Generation and Refining with Coarse-to-fine Pose-Reversible Guidance
Text-to-image diffusion models have significantly advanced in conditional image generation. However, these models usually struggle with accurately rendering images featuring humans, resulting in distorted limbs and other…
BenchmarkingConditional Image GenerationDiagnosticImage Generation+2Representation learning with CGAN for casual inference
Conditional Generative Adversarial Nets (CGAN) is often used to improve conditional image generation performance. However, there is little research on Representation learning with CGAN for causal inference. This paper pr…
Causal InferenceConditional Image GenerationImage GenerationRepresentation LearningToward a Diffusion-Based Generalist for Dense Vision Tasks
Building generalized models that can solve many computer vision tasks simultaneously is an intriguing direction. Recent works have shown image itself can be used as a natural interface for general-purpose visual percepti…
Conditional Image GenerationImage GenerationQuantization