Cover Image

Matryoshka Diffusion Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gu, Jiatao, Zhai, Shuangfei, Zhang, Yizhe, Susskind, Josh, Jaitly, Navdeep
Format:	Preprint
Published:	2023
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improving GFlowNets for Text-to-Image Diffusion Alignment
by: Zhang, Dinghuai, et al.
Published: (2024)

DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation
by: Gu, Jiatao, et al.
Published: (2024)

Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling
by: Gu, Jiatao, et al.
Published: (2024)

How Far Are We from Intelligent Visual Deductive Reasoning?
by: Zhang, Yizhe, et al.
Published: (2024)

TypeScore: A Text Fidelity Metric for Text-to-Image Generative Models
by: Sampaio, Georgia Gabriela, et al.
Published: (2024)

Normalizing Flows are Capable Generative Models
by: Zhai, Shuangfei, et al.
Published: (2024)

Normalizing Trajectory Models
by: Gu, Jiatao, et al.
Published: (2026)

Many-to-many Image Generation with Auto-regressive Diffusion Models
by: Shen, Ying, et al.
Published: (2024)

STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
by: Shen, Ying, et al.
Published: (2026)

Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP
by: Huang, Chen, et al.
Published: (2024)

PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model
by: Zhang, Yizhe, et al.
Published: (2023)

STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows
by: Gu, Jiatao, et al.
Published: (2025)

The Coupling Within: Flow Matching via Distilled Normalizing Flows
by: Berthelot, David, et al.
Published: (2026)

Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows
by: Zhang, Ruixiang, et al.
Published: (2025)

STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
by: Gu, Jiatao, et al.
Published: (2025)

World-consistent Video Diffusion with Explicit 3D Modeling
by: Zhang, Qihang, et al.
Published: (2024)

Normalizing Flows with Iterative Denoising
by: Chen, Tianrong, et al.
Published: (2026)

Target Concrete Score Matching: A Holistic Framework for Discrete Diffusion
by: Zhang, Ruixiang, et al.
Published: (2025)

Matryoshka Representation Learning
by: Kusupati, Aditya, et al.
Published: (2022)

Matryoshka Multimodal Models
by: Cai, Mu, et al.
Published: (2024)

Text-Conditional JEPA for Learning Semantically Rich Visual Representations
by: Huang, Chen, et al.
Published: (2026)

Matryoshka Query Transformer for Large Vision-Language Models
by: Hu, Wenbo, et al.
Published: (2024)

MatMamba: A Matryoshka State Space Model
by: Shukla, Abhinav, et al.
Published: (2024)

TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics
by: Chen, Tianrong, et al.
Published: (2025)

How PARTs assemble into wholes: Learning the relative composition of images
by: Ayoughi, Melika, et al.
Published: (2025)

How does My Model Fail? Automatic Identification and Interpretation of Physical Plausibility Failure Modes with Matryoshka Transcoders
by: Tang, Yiming, et al.
Published: (2025)

Rethinking JEPA: Compute-Efficient Video SSL with Frozen Teachers
by: Li, Xianhang, et al.
Published: (2025)

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting
by: Huang, Chen, et al.
Published: (2025)

DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models
by: Miao, Kevin, et al.
Published: (2024)

Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games
by: Zhang, Yizhe, et al.
Published: (2023)

Divide-or-Conquer? Which Part Should You Distill Your LLM?
by: Wu, Zhuofeng, et al.
Published: (2024)

Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling
by: Zheng, Huangjie, et al.
Published: (2025)

Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
by: Wen, Tiansheng, et al.
Published: (2025)

Incorporating Pre-trained Diffusion Models in Solving the Schrödinger Bridge Problem
by: Tang, Zhicong, et al.
Published: (2025)

Roots Beneath the Cut: Uncovering the Risk of Concept Revival in Pruning-Based Unlearning for Diffusion Models
by: Zhang, Ci, et al.
Published: (2026)

Score Distillation of Flow Matching Models
by: Zhou, Mingyuan, et al.
Published: (2025)

Federated Adversarial Domain Adaptation
by: Peng, Xingchao, et al.
Published: (2019)

Diffusion-RPO: Aligning Diffusion Models through Relative Preference Optimization
by: Gu, Yi, et al.
Published: (2024)

What Makes the Preferred Thinking Direction for LLMs in Multiple-choice Questions?
by: Zhang, Yizhe, et al.
Published: (2025)

Spatial Lifting for Dense Prediction
by: Xu, Mingzhi, et al.
Published: (2025)