FlowBlending: Multi-Model Sampling for Fast, High-Fidelity AI Video

Описание: Generating high-quality AI video is expensive — and most acceleration methods trade speed for visual quality.
FlowBlending takes a smarter approach.

Instead of relying on a single model for the entire diffusion process, FlowBlending uses stage-aware multi-model sampling, dynamically allocating compute where it matters most.

How it works:
• Large model at early stages → establishes global semantics and structure
• Smaller model in the middle → where quality divergence is minimal and speed gains are highest
• Large model again at late stages → refines details and restores high-frequency fidelity

This simple but effective strategy delivers:
• Up to 1.65× faster inference
• 57.35% reduction in FLOPs
• Preserved visual quality, temporal consistency, and semantic alignment
• Compatibility with existing sampling acceleration methods — enabling up to 2× additional speedup

In the demo comparisons:
• Video 1: generated with a small model only
• Video 2: generated with a large model only
• Video 3: generated with FlowBlending
The result shows that smart orchestration beats brute force.

No code released yet — but this is a strong signal toward more efficient, modular video generation pipelines.

Project page: https://jibin86.github.io/flowblendin...


#AIVideo #VideoGeneration #DiffusionModels
#FlowBlending #GenerativeAI #ComputerVision
#MachineLearning #ModelOptimization #AIResearch
#VideoSynthesis #MultimodalAI