853 citations · 853 across the 2 of their papers we have counts for
10 papers · 1 filter
Finite Difference Flow Optimization for RL Post-Training of Text-to-Image Models
David McAllister, Miika Aittala, Tero Karras +4
Reinforcement learning (RL) has become a standard technique for post-training diffusion-based image synthesis models, as it enables learning from reward signals to explicitly impro…
Guiding a Diffusion Model with a Bad Version of Itself
Tero Karras, Miika Aittala, Tuomas Kynkäänniemi +3
The primary axes of interest in image-generating diffusion models are image quality, the amount of variation in the results, and how well the results align with a given condition,…
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
Tuomas Kynkäänniemi, Miika Aittala, Tero Karras +3
Guidance is a crucial technique for extracting the best performance out of image-generating diffusion models. Traditionally, a constant guidance weight has been applied throughout…
Analyzing and Improving the Training Dynamics of Diffusion Models
Tero Karras, Miika Aittala, Jaakko Lehtinen +3
Diffusion models currently dominate the field of data-driven image synthesis with their unparalleled scaling to large datasets. In this paper, we identify and rectify several cause…
Alias-Free Generative Adversarial Networks
Tero Karras, Miika Aittala, Samuli Laine +4
We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unheal…
Training Generative Adversarial Networks with Limited Data
Tero Karras, Miika Aittala, Janne Hellsten +3
Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator…