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cloud.google.com·8h ago
AI Research
arXiv CS.AI·7d ago·5 min read
Geometry-Aware Time Reparameterization for Flow-Map Distillation
TL;DR
A geometry-aware time reparameterization method enhances flow-map distillation by allocating more time to challenging trajectory segments, leading to improved image generation quality. Experiments show significant gains in one- and two-step generation tasks across various datasets.
✦ Why It Matters
Implement geometry-aware time reparameterization in your flow-map distillation projects to improve image generation quality.
Key Takeaways
- Explore geometry-aware time reparameterization for better distillation outcomes.
- Focus on trajectory segments with high normal acceleration during training.
- Utilize the derived shared clock for effective time allocation.
- Test improvements in image generation on datasets like CIFAR-10 and CelebA-64.
How It Works
The proposed method involves a shared clock that adjusts the time allocation for the student model based on the normal acceleration of the teacher's trajectory. By focusing more time on segments with high normal acceleration, the student can better learn the complex transitions, leading to improved sample quality.