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We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions.
Uncovering the disentanglement capability in text-to-image diffusion models
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T. Chen, Y. Sun, Q. Xiao, and W. Yin · 2022
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Diffusion models already have a semantic latent space
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Aligning text-to-image models using human feedback
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Uniform-in-time propagation of chaos for the mean-field gradient langevin dynamics
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End-to-end diffusion latent optimization improves classifier guidance
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Unsupervised discovery of interpretable directions in h-space of pre-trained diffusion models
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Training diffusion models with reinforcement learning
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