5 papers
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…
A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation
Andrew Z. Wang, Songwei Ge, Tero Karras +2
Both text-to-image generation and large language models (LLMs) have made significant advancements. However, many text-to-image models still employ the somewhat outdated T5 and CLIP…
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,…
Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models
NVIDIA, :, Yuval Atzmon +29
We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space dif…
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…