TREAD: Token Routing for Efficient Diffusion Training (Mar 2025)

Описание: Title: TREAD: Token Routing for Efficient Architecture-agnostic Diffusion Training (Jan 2025)
Link: http://arxiv.org/abs/2501.04765v2
Date: January 2025

Summary:
TREAD introduces a training strategy for diffusion models that uses token routing to improve training efficiency and generative performance. It transports tokens from early layers to deeper layers, applicable to transformer-based and state-space models without architectural changes or extra parameters. Results show improvements in convergence speed and FID on ImageNet-256.

Key Topics:
Diffusion Models
Token Routing
Training Efficiency
Generative Performance
Architecture-agnostic Training

Chapters:
00:00 - Introduction to Tread
00:16 - The Bottleneck
00:37 - A Double Win
00:54 - Tread Explained
01:15 - Express Lane Analogy
01:27 - Speed Up
01:48 - Democratizing AI R&D
02:15 - Computational Beast
02:37 - Staggering Numbers
03:17 - Improving Efficiency
03:47 - Existing Methods
04:18 - Token Routing
04:42 - Dynamic Transport Mechanism
05:16 - Skipping Steps
05:44 - Smart Shortcut
06:16 - Token Selection
06:33 - Only During Training
07:00 - Overall Performance
07:25 - Quadratic Gains
07:51 - Counter-Intuitive Part
08:31 - Beneficial Challenge
08:56 - Resilient Representations
09:28 - Empirical Results
09:55 - Guided Setting
10:25 - Faster Iterations
10:41 - Architecturally Agnostic
11:13 - Modularity
11:34 - Does it Scale Well?
11:53 - Practical Guidance
12:15 - Route Location
12:37 - There's a Catch
12:56 - Processing Room
13:14 - Monitor
13:20 - Selection Rate
13:59 - Bigger Models
14:45 - Dug into Tread
15:25 - What does this mean for you?
15:53 - About resource allocation
16:14 - The Code