257 citations · 299 across the 19 of their papers we have counts for
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Elucidating The Design Space of Classifier-Guided Diffusion Generation
Jiajun Ma, Tianyang Hu, Wenjia Wang +1
Guidance in conditional diffusion generation is of great importance for sample quality and controllability. However, existing guidance schemes are to be desired. On one hand, mains…
SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
Shuchen Xue, Mingyang Yi, Weijian Luo +4
Diffusion Probabilistic Models (DPMs) have achieved considerable success in generation tasks. As sampling from DPMs is equivalent to solving diffusion SDE or ODE which is time-cons…
Training Energy-Based Models with Diffusion Contrastive Divergences
Weijian Luo, Hao Jiang, Tianyang Hu +3
Energy-Based Models (EBMs) have been widely used for generative modeling. Contrastive Divergence (CD), a prevailing training objective for EBMs, requires sampling from the EBM with…
Complexity Matters: Rethinking the Latent Space for Generative Modeling
Tianyang Hu, Fei Chen, Haonan Wang +4
In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion models the latent space induced by an encoder and generates i…
On the Generalization of Diffusion Model
Mingyang Yi, Jiacheng Sun, Zhenguo Li
The diffusion probabilistic generative models are widely used to generate high-quality data. Though they can synthetic data that does not exist in the training set, the rationale b…
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
Weijian Luo, Tianyang Hu, Shifeng Zhang +3
Due to the ease of training, ability to scale, and high sample quality, diffusion models (DMs) have become the preferred option for generative modeling, with numerous pre-trained m…