Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
An Analysis on Quantizing Diffusion Transformers
Diffusion Models (DMs) utilize an iterative denoising process to transform random noise into synthetic data. Initally proposed with a UNet structure, DMs excel at producing images that are virtually indistinguishable wit…
Conditional Image GenerationDenoisingImage GenerationQuantizationEMMA: Your Text-to-Image Diffusion Model Can Secretly Accept Multi-Modal Prompts
Recent advancements in image generation have enabled the creation of high-quality images from text conditions. However, when facing multi-modal conditions, such as text combined with reference appearances, existing metho…
Conditional Image GenerationImage GenerationAutoregressive Model Beats Diffusion: Llama for Scalable Image Generation
We introduce LlamaGen, a new family of image generation models that apply original ``next-token prediction'' paradigm of large language models to visual generation domain. It is an affirmative answer to whether vanilla a…
Conditional Image GenerationImage GenerationImage ReconstructionOmniControlNet: Dual-stage Integration for Conditional Image Generation
We provide a two-way integration for the widely adopted ControlNet by integrating external condition generation algorithms into a single dense prediction method and incorporating its individually trained image generation…
Conditional Image GenerationImage GenerationText to Image GenerationText-to-Image GenerationMedical Vision Generalist: Unifying Medical Imaging Tasks in Context
This study presents Medical Vision Generalist (MVG), the first foundation model capable of handling various medical imaging tasks -- such as cross-modal synthesis, image segmentation, denoising, and inpainting -- within …
Conditional Image GenerationDenoisingImage GenerationImage Segmentation+1Frame Interpolation with Consecutive Brownian Bridge Diffusion
Recent work in Video Frame Interpolation (VFI) tries to formulate VFI as a diffusion-based conditional image generation problem, synthesizing the intermediate frame given a random noise and neighboring frames. Due to the…
Conditional Image GenerationImage GenerationVideo Frame InterpolationImproving Training-free Conditional Diffusion Model via Fisher Information
Training-free conditional diffusion models have received great attention in conditional image generation tasks. However, they require a computationally expensive conditional score estimator to let the intermediate result…
Conditional Image GenerationImage GenerationInformativenessWatermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models
Diffusion Models (DMs) have shown remarkable capabilities in various image-generation tasks. However, there are growing concerns that DMs could be used to imitate unauthorized creations and thus raise copyright issues. T…
Conditional Image GenerationImage GenerationIn-Context Translation: Towards Unifying Image Recognition, Processing, and Generation
We propose In-Context Translation (ICT), a general learning framework to unify visual recognition (e.g., semantic segmentation), low-level image processing (e.g., denoising), and conditional image generation (e.g., edge-…
Conditional Image GenerationDenoisingImage GenerationIn-Context Learning+4Condition-Aware Neural Network for Controlled Image Generation
We present Condition-Aware Neural Network (CAN), a new method for adding control to image generative models. In parallel to prior conditional control methods, CAN controls the image generation process by dynamically mani…
Conditional Image GenerationImage GenerationText to Image GenerationText-to-Image GenerationBenchmarking Counterfactual Image Generation
Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in domains such as natural image or medical…
BenchmarkingConditional Image GenerationcounterfactualImage GenerationConditional Wasserstein Distances with Applications in Bayesian OT Flow Matching
In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation. While this approach also controls the distance b…
Conditional Image GenerationImage GenerationCLIP-VQDiffusion : Langauge Free Training of Text To Image generation using CLIP and vector quantized diffusion model
There has been a significant progress in text conditional image generation models. Recent advancements in this field depend not only on improvements in model structures, but also vast quantities of text-image paired data…
Conditional Image GenerationImage GenerationText to Image GenerationText-to-Image GenerationAttack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation
Existing generative adversarial network (GAN) based conditional image generative models typically produce fixed output for the same conditional input, which is unreasonable for highly subjective tasks, such as large-mask…
Adversarial AttackConditional Image GenerationGenerative Adversarial NetworkImage Generation+2Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models
This paper introduces Bespoke Non-Stationary (BNS) Solvers, a solver distillation approach to improve sample efficiency of Diffusion and Flow models. BNS solvers are based on a family of non-stationary solvers that prova…
Audio GenerationConditional Image GenerationDiversityImage Generation+2Rethinking cluster-conditioned diffusion models for label-free image synthesis
Diffusion-based image generation models can enhance image quality when conditioned on ground truth labels. Here, we conduct a comprehensive experimental study on image-level conditioning for diffusion models using cluste…
ClusteringConditional Image GenerationImage ClusteringImage GenerationFineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 Classes
The class-conditional image generation based on diffusion models is renowned for generating high-quality and diverse images. However, most prior efforts focus on generating images for general categories, e.g., 1000 class…
Conditional Image GenerationImage Generationparameter-efficient fine-tuningStructure-Guided Adversarial Training of Diffusion Models
Diffusion models have demonstrated exceptional efficacy in various generative applications. While existing models focus on minimizing a weighted sum of denoising score matching losses for data distribution modeling, thei…
Conditional Image GenerationDenoisingImage GenerationStochastic Conditional Diffusion Models for Robust Semantic Image Synthesis
Semantic image synthesis (SIS) is a task to generate realistic images corresponding to semantic maps (labels). However, in real-world applications, SIS often encounters noisy user inputs. To address this, we propose Stoc…
Conditional Image GenerationImage GenerationImage-to-Image TranslationNoisy Semantic Image SynthesisScalable Diffusion Models with State Space Backbone
This paper presents a new exploration into a category of diffusion models built upon state space architecture. We endeavor to train diffusion models for image data, wherein the traditional U-Net backbone is supplanted by…
Conditional Image GenerationImage GenerationState Space Models