ViBT Image Generation: How Brownian Bridge Models Achieve 4× Faster AI Inference
21 days ago 高效码农ViBT: Vision Bridge Transformer at Scale – A Practical Deep Dive What is ViBT and why does it achieve up to 4× faster inference than token-heavy conditional diffusion models while maintaining comparable quality? ViBT is the first large-scale realization of Brownian Bridge generative models for vision tasks. Instead of the classic “noise-to-data” paradigm, it directly learns stochastic trajectories from a structured source (image/video) to a structured target, eliminating most conditioning tokens and dramatically reducing compute. Figure: Example results of ViBT across instruction-based editing, stylization, colorization, and frame interpolation. Why the Noise-to-Data Paradigm Feels Wrong for Conditional Generation Most modern image …
Decoupled DMD: How 8-Step Diffusion Outperforms 100-Step Models Without Extra Parameters
25 days ago 高效码农Decoupled DMD: Why 8-Step Diffusion Can Outperform 100-Step Teachers Without Extra Parameters Central question: How can a student network with no additional parameters generate images that look better than its 100-step teacher in only 8 forward passes? Short answer: By decomposing the training objective into two cooperative mechanisms—CFG Augmentation (the engine) and Distribution Matching (the seat-belt)—and giving each its own noise schedule. 1. The Misleading Success of DMD Core question: If DMD was supposed to match distributions, why does it only work when you add an asymmetric CFG term that breaks the theory? Short answer: Theory describes the DM term; …
SongBloom: Revolutionizing AI Music with Interleaved Autoregressive Diffusion
1 months ago 高效码农SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement Music generation has long captivated researchers and creators alike, but producing full-length songs with coherent structure, harmonious vocals, and rich accompaniment remains a formidable challenge. SongBloom emerges as a novel framework that seamlessly blends autoregressive language models with diffusion-based refinement, enabling the generation of high-quality songs up to 150 seconds long. This article explores how SongBloom’s innovative interleaved generation paradigm addresses the core limitations of existing approaches, delivering state-of-the-art performance in both subjective and objective evaluations. The Challenge of Long-Form Song Generation Why is generating coherent, full-length songs so …
Solving Spatial Confusion: How CoMPaSS Transforms Text-to-Image Diffusion Models
3 months ago 高效码农CoMPaSS: A Framework for Better Spatial Understanding in Text-to-Image Models Hey there, if you’re into text-to-image generation, you’ve probably noticed how these models can create stunning, realistic pictures from just a description. But have you ever wondered why they sometimes mess up simple things like “a cat to the left of a dog”? It turns out, getting spatial relationships right—like left, right, above, or below—is trickier than it seems. That’s where CoMPaSS comes in. It’s a framework designed to help existing diffusion models handle these spatial details more accurately. In this post, I’ll walk you through what CoMPaSS is, how …
Why Fourier Space Reveals the Hidden Truth About Diffusion Models’ Detail Generation
6 months ago 高效码农Fourier Space Perspective on Diffusion Models: Why High-Frequency Detail Generation Matters 1. Fundamental Principles of Diffusion Models Diffusion models have revolutionized generative AI across domains like image synthesis, video generation, and protein structure prediction. These models operate through two key phases: 1.1 Standard DDPM Workflow Forward Process (Noise Addition): x_t = √(ᾱ_t)x_0 + √(1-ᾱ_t)ε Progressively adds isotropic Gaussian noise Controlled by decreasing noise schedule ᾱ_t Reverse Process (Denoising): Starts from pure noise (x_T ∼ N(0,I)) Uses U-Net to iteratively predict clean data 2. Key Insights from Fourier Analysis Transitioning to Fourier space reveals critical frequency-dependent behaviors: 2.1 Spectral Properties of Natural Data Data Type …