Core Concepts
Pyramid Diffusion Model enables ultra-high-resolution image synthesis through innovative architecture and latent representation.
Abstract
Introduction:
Diffusion models' success in generative tasks.
Latent Diffusion Model's efficiency in generating large images.
Limitations of Single Latent:
Single latent restricts network design space.
Deeper networks yield better performance.
Proposed Solution: Pyramid Latent Structure:
Pyramid latent structure with varied resolutions.
Enables reconstruction from semantic concepts to details.
Enhancements in Neural Network Components:
Spatial-Channel Attention, Res-Skip Connection, Spectral Norm, Decreasing Dropout Strategy.
Key Achievements of Pyramid Diffusion Model:
Replacing single latent with pyramid latents for flexible design choices.
Introducing Pyramid UNet with branches for modeling pyramid structures.
Stats
表示された画像には2048*1024ピクセルが含まれています。
Quotes
"Pyramid Diffusion Model achieves the synthesis of images with a 2K resolution for the first time."
"Our framework takes advantage of deep and wide neural networks as well as a larger number of channels."