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Is Noise Conditioning Necessary for Denoising Generative Models?
Paper • 2502.13129 • Published • 2 -
REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
Paper • 2504.10483 • Published • 22 -
Mean Flows for One-step Generative Modeling
Paper • 2505.13447 • Published • 7 -
Latent Diffusion Model without Variational Autoencoder
Paper • 2510.15301 • Published • 50
Bo Lin
linbo0518
AI & ML interests
Computer Vision
Organizations
None yet
Diffusion Language Models
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Structured Denoising Diffusion Models in Discrete State-Spaces
Paper • 2107.03006 • Published • 2 -
Simplified and Generalized Masked Diffusion for Discrete Data
Paper • 2406.04329 • Published • 8 -
Simple and Effective Masked Diffusion Language Models
Paper • 2406.07524 • Published • 12 -
Large Language Diffusion Models
Paper • 2502.09992 • Published • 128
ToRead
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Is Noise Conditioning Necessary for Denoising Generative Models?
Paper • 2502.13129 • Published • 2 -
REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
Paper • 2504.10483 • Published • 22 -
Mean Flows for One-step Generative Modeling
Paper • 2505.13447 • Published • 7 -
Latent Diffusion Model without Variational Autoencoder
Paper • 2510.15301 • Published • 50
Diffusion Language Models
-
Structured Denoising Diffusion Models in Discrete State-Spaces
Paper • 2107.03006 • Published • 2 -
Simplified and Generalized Masked Diffusion for Discrete Data
Paper • 2406.04329 • Published • 8 -
Simple and Effective Masked Diffusion Language Models
Paper • 2406.07524 • Published • 12 -
Large Language Diffusion Models
Paper • 2502.09992 • Published • 128
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datasets 0
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