Centrala begrepp
YOSO introduces a novel generative model for high-quality one-step image synthesis by integrating diffusion process with GANs.
Sammanfattning
The content introduces YOSO, a generative model for rapid, scalable, and high-fidelity one-step image synthesis. It combines diffusion process with GANs to achieve competitive performance in training from scratch and fine-tuning pre-trained text-to-image diffusion models. The method is detailed through various experiments and comparisons with existing models.
Abstract:
- YOSO introduced as a generative model for one-step image synthesis.
- Integration of diffusion process with GANs for competitive performance.
- Capable of training from scratch and fine-tuning pre-trained models.
Introduction:
- Diffusion models demonstrated state-of-the-art results in generative tasks.
- Generation speed limitations due to iterative denoising.
- Comparison between DMs and GANs for large-scale datasets.
Method: Self-Cooperative Diffusion GANs:
- Proposal to directly construct learning objectives over clean data.
- Formulation of optimization objective combining adversarial divergence and KL divergence.
- Training objective formulated for stable training and effective learning.
Experiments:
- Evaluation on unconditional image generation using CIFAR-10 dataset.
- Ablation studies on the effect of consistency loss, LPIPS loss, and adversarial divergence.
- Text-to-image generation results using PixArt-alpha model fine-tuned with YOSO.
Application:
- Demonstration of YOSO's capability in various downstream tasks like image-to-image editing and compatibility with different base models.
Citat
"We introduce YOSO, a novel generative model designed for rapid, scalable, and high-fidelity one-step image synthesis."
"Our work presents several significant contributions: We introduce YOSO, a novel generative model that can generate high-quality images with one-step inference."