{"list":"closest","total":114,"rows":[{"id":"2306.02174","title":"Training Data Attribution for Diffusion Models","created":"2023-06-03","cited_by_count":24},{"id":"2306.02063","title":"Exploring the Optimal Choice for Generative Processes in Diffusion Models: Ordinary vs Stochastic Differential Equations","created":"2023-06-03","cited_by_count":15},{"id":"2306.01900","title":"Conditional Generation from Unconditional Diffusion Models using Denoiser Representations","created":"2023-06-02","cited_by_count":7},{"id":"2305.20086","title":"Understanding and Mitigating Copying in Diffusion Models","created":"2023-05-31","cited_by_count":209},{"id":"2305.19947","title":"A Geometric Perspective on Diffusion Models","created":"2023-05-31","cited_by_count":18},{"id":"2305.19693","title":"Spontaneous Symmetry Breaking in Generative Diffusion Models","created":"2023-05-31","cited_by_count":80},{"id":"2305.19518","title":"Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels","created":"2023-05-31","cited_by_count":20},{"id":"2305.16317","title":"Parallel Sampling of Diffusion Models","created":"2023-05-25","cited_by_count":112},{"id":"2305.16261","title":"Trans-Dimensional Generative Modeling via Jump Diffusion Models","created":"2023-05-25","cited_by_count":35},{"id":"2305.16150","title":"Unifying GANs and Score-Based Diffusion as Generative Particle Models","created":"2023-05-25","cited_by_count":21},{"id":"2305.15712","title":"Knowledge Diffusion for Distillation","created":"2023-05-25","cited_by_count":53},{"id":"2305.15583","title":"Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time Steps","created":"2023-05-24","cited_by_count":73},{"id":"2305.14712","title":"On the Generalization of Diffusion Model","created":"2023-05-24","cited_by_count":30},{"id":"2305.14677","title":"Optimal Linear Subspace Search: Learning to Construct Fast and High-Quality Schedulers for Diffusion Models","created":"2023-05-24","cited_by_count":12},{"id":"2305.13128","title":"GSURE-Based Diffusion Model Training with Corrupted Data","created":"2023-05-22","cited_by_count":40},{"id":"2305.12954","title":"Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?","created":"2023-05-22","cited_by_count":21},{"id":"2305.11089","title":"Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces","created":"2023-05-18","cited_by_count":30},{"id":"2305.10924","title":"Structural Pruning for Diffusion Models","created":"2023-05-18","cited_by_count":195},{"id":"2305.09847","title":"Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important?","created":"2023-05-16","cited_by_count":5},{"id":"2305.08891","title":"Common Diffusion Noise Schedules and Sample Steps are Flawed","created":"2023-05-15","cited_by_count":310},{"id":"2305.08092","title":"Meta-DM: Applications of Diffusion Models on Few-Shot Learning","created":"2023-05-14","cited_by_count":4},{"id":"2305.06710","title":"Null-text Guidance in Diffusion Models is Secretly a Cartoon-style Creator","created":"2023-05-11","cited_by_count":12},{"id":"2305.03935","title":"Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs","created":"2023-05-06","cited_by_count":54},{"id":"2304.04262","title":"A Comprehensive Survey on Knowledge Distillation of Diffusion Models","created":"2023-04-09","cited_by_count":45},{"id":"2303.18181","title":"A Closer Look at Parameter-Efficient Tuning in Diffusion Models","created":"2023-03-31","cited_by_count":15},{"id":"2303.14389","title":"MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer","created":"2023-03-25","cited_by_count":262},{"id":"2303.13703","title":"End-to-End Diffusion Latent Optimization Improves Classifier Guidance","created":"2023-03-23","cited_by_count":108},{"id":"2303.06555","title":"One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale","created":"2023-03-12","cited_by_count":187},{"id":"2303.01861","title":"Diffusion Models are Minimax Optimal Distribution Estimators","created":"2023-03-03","cited_by_count":165},{"id":"2303.01469","title":"Consistency Models","created":"2023-03-02","cited_by_count":1822},{"id":"2303.00165","title":"Diffusion Probabilistic Fields","created":"2023-03-01","cited_by_count":28},{"id":"2302.13834","title":"Denoising Diffusion Samplers","created":"2023-02-27","cited_by_count":149},{"id":"2302.12469","title":"Unsupervised Discovery of Semantic Latent Directions in Diffusion Models","created":"2023-02-24","cited_by_count":28},{"id":"2302.10688","title":"On Calibrating Diffusion Probabilistic Models","created":"2023-02-21","cited_by_count":3},{"id":"2302.10586","title":"Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels","created":"2023-02-21","cited_by_count":37},{"id":"2302.07121","title":"Universal Guidance for Diffusion Models","created":"2023-02-14","cited_by_count":437},{"id":"2301.13622","title":"Learning Data Representations with Joint Diffusion Models","created":"2023-01-31","cited_by_count":22},{"id":"2301.12334","title":"Don't Play Favorites: Minority Guidance for Diffusion Models","created":"2023-01-29","cited_by_count":37},{"id":"2301.10972","title":"On the Importance of Noise Scheduling for Diffusion Models","created":"2023-01-26","cited_by_count":194},{"id":"2301.11108","title":"On the Mathematics of Diffusion Models","created":"2023-01-25","cited_by_count":10},{"id":"2212.13771","title":"Exploring Vision Transformers as Diffusion Learners","created":"2022-12-28","cited_by_count":10},{"id":"2212.14678","title":"Exploring Transformer Backbones for Image Diffusion Models","created":"2022-12-27","cited_by_count":3},{"id":"2212.09748","title":"Scalable Diffusion Models with Transformers","created":"2022-12-19","cited_by_count":6054},{"id":"2211.16152","title":"Wavelet Diffusion Models are fast and scalable Image Generators","created":"2022-11-29","cited_by_count":117},{"id":"2211.13449","title":"Fast Sampling of Diffusion Models via Operator Learning","created":"2022-11-24","cited_by_count":175},{"id":"2211.12039","title":"Accelerating Diffusion Sampling with Classifier-based Feature Distillation","created":"2022-11-22","cited_by_count":15},{"id":"2211.03264","title":"Few-shot Image Generation with Diffusion Models","created":"2022-11-07","cited_by_count":21},{"id":"2211.01095","title":"DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models","created":"2022-11-02","cited_by_count":938},{"id":"2210.12254","title":"Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models","created":"2022-10-21","cited_by_count":17},{"id":"2210.12192","title":"Conditional Diffusion with Less Explicit Guidance via Model Predictive Control","created":"2022-10-21","cited_by_count":1},{"id":"2210.10960","title":"Diffusion Models already have a Semantic Latent Space","created":"2022-10-20","cited_by_count":328},{"id":"2210.06462","title":"Self-Guided Diffusion Models","created":"2022-10-12","cited_by_count":37},{"id":"2210.05559","title":"Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance","created":"2022-10-11","cited_by_count":67},{"id":"2210.09292","title":"Efficient Diffusion Models for Vision: A Survey","created":"2022-10-07","cited_by_count":51},{"id":"2210.03142","title":"On Distillation of Guided Diffusion Models","created":"2022-10-06","cited_by_count":725},{"id":"2210.00939","title":"Improving Sample Quality of Diffusion Models Using Self-Attention Guidance","created":"2022-10-03","cited_by_count":154},{"id":"2209.12152","title":"All are Worth Words: A ViT Backbone for Diffusion Models","created":"2022-09-25","cited_by_count":502},{"id":"2209.11215","title":"Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions","created":"2022-09-22","cited_by_count":445},{"id":"2209.05442","title":"Soft Diffusion: Score Matching for General Corruptions","created":"2022-09-12","cited_by_count":102},{"id":"2209.04747","title":"Diffusion Models in Vision: A Survey","created":"2022-09-10","cited_by_count":1015},{"id":"2209.02646","title":"A Survey on Generative Diffusion Model","created":"2022-09-06","cited_by_count":240},{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114},{"id":"2208.11970","title":"Understanding Diffusion Models: A Unified Perspective","created":"2022-08-25","cited_by_count":340},{"id":"2208.09392","title":"Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise","created":"2022-08-19","cited_by_count":276},{"id":"2208.08664","title":"Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance","created":"2022-08-18","cited_by_count":33},{"id":"2208.07949","title":"Riemannian Diffusion Models","created":"2022-08-16","cited_by_count":122},{"id":"2208.07791","title":"Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model","created":"2022-08-16","cited_by_count":58},{"id":"2208.05314","title":"Convergence of denoising diffusion models under the manifold hypothesis","created":"2022-08-10","cited_by_count":242},{"id":"2208.04202","title":"Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning","created":"2022-08-08","cited_by_count":373},{"id":"2207.12598","title":"Classifier-Free Diffusion Guidance","created":"2022-07-26","cited_by_count":5847},{"id":"2207.09786","title":"Non-Uniform Diffusion Models","created":"2022-07-20","cited_by_count":14},{"id":"2206.07309","title":"Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models","created":"2022-06-15","cited_by_count":83},{"id":"2206.07275","title":"CARD: Classification and Regression Diffusion Models","created":"2022-06-15","cited_by_count":103},{"id":"2206.05173","title":"How Much is Enough? A Study on Diffusion Times in Score-based Generative Models","created":"2022-06-10","cited_by_count":35},{"id":"2206.00927","title":"DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps","created":"2022-06-02","cited_by_count":1979},{"id":"2206.00364","title":"Elucidating the Design Space of Diffusion-Based Generative Models","created":"2022-06-01","cited_by_count":3045},{"id":"2205.15463","title":"Few-Shot Diffusion Models","created":"2022-05-30","cited_by_count":38},{"id":"2205.13699","title":"Maximum Likelihood Training of Implicit Nonlinear Diffusion Models","created":"2022-05-27","cited_by_count":42},{"id":"2205.12524","title":"Accelerating Diffusion Models via Early Stop of the Diffusion Process","created":"2022-05-25","cited_by_count":100},{"id":"2205.01490","title":"Subspace Diffusion Generative Models","created":"2022-05-03","cited_by_count":70},{"id":"2204.13902","title":"Fast Sampling of Diffusion Models with Exponential Integrator","created":"2022-04-29","cited_by_count":532},{"id":"2204.00227","title":"Perception Prioritized Training of Diffusion Models","created":"2022-04-01","cited_by_count":303},{"id":"2203.17260","title":"Generating High Fidelity Data from Low-density Regions using Diffusion Models","created":"2022-03-31","cited_by_count":78},{"id":"2203.04304","title":"Dynamic Dual-Output Diffusion Models","created":"2022-03-08","cited_by_count":25},{"id":"2202.09778","title":"Pseudo Numerical Methods for Diffusion Models on Manifolds","created":"2022-02-20","cited_by_count":700},{"id":"2202.05830","title":"Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Quality","created":"2022-02-11","cited_by_count":182},{"id":"2202.02763","title":"Riemannian Score-Based Generative Modelling","created":"2022-02-06","cited_by_count":219},{"id":"2201.06503","title":"Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models","created":"2022-01-17","cited_by_count":334},{"id":"2201.00308","title":"DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents","created":"2022-01-02","cited_by_count":104},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"2112.07804","title":"Tackling the Generative Learning Trilemma with Denoising Diffusion GANs","created":"2021-12-15","cited_by_count":516},{"id":"2112.07068","title":"Score-Based Generative Modeling with Critically-Damped Langevin Diffusion","created":"2021-12-14","cited_by_count":233},{"id":"2111.15640","title":"Diffusion Autoencoders: Toward a Meaningful and Decodable Representation","created":"2021-11-30","cited_by_count":449},{"id":"2111.13606","title":"Conditional Image Generation with Score-Based Diffusion Models","created":"2021-11-26","cited_by_count":197},{"id":"2111.12701","title":"Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes","created":"2021-11-24","cited_by_count":71},{"id":"2110.05948","title":"Denoising Diffusion Gamma Models","created":"2021-10-10","cited_by_count":27},{"id":"2107.00630","title":"Variational Diffusion Models","created":"2021-07-01","cited_by_count":1153},{"id":"2106.07582","title":"Non Gaussian Denoising Diffusion Models","created":"2021-06-14","cited_by_count":45},{"id":"2106.05931","title":"Score-based Generative Modeling in Latent Space","created":"2021-06-10","cited_by_count":636},{"id":"2106.05527","title":"Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation","created":"2021-06-10","cited_by_count":97}],"next_cursor":"100"}