PDD: Ultra-Simple Distillation for Few-Step Video Generation
ArashVahdat · x · 2026-07-30
A researcher highlights Parallel Decoding Distillation (PDD), an exceptionally simple distillation method for accelerating diffusion and flow models.
Key Advantages:
- Achieves state-of-the-art quality and diversity with variable NFE.
- First to scale trajectory-distillation to few-step video generation.
Minimalist Design:
PDD is extremely cheap and simple, relying on a single, regression-based MSE loss using 1 student and 1 detached teacher forward pass. It requires NO data, NO adversarial loss, NO JVP/finite-difference approximations, NO additional trainable models, and NO stabilization tricks.
More from Multimodal
- Claude Opus 5.5 makes a pure-code Halloween animation starring bots trick-or-treating for tokens — sattvi · 2026-10-16
- 1,290-line commit lands MOSS Image support in ostris/ai-toolkit — ostrisai · 2026-10-16
- Ostris AI Toolkit adds training support for OpenMOSS's MOSS-Image-SFT model — ostrisai · 2026-10-16
- Nokia vs iPhone, car edition: AI image shows Tesla Roadster hovering over a Porsche — vaibhavbetter · 2026-10-16
- Layer-and-mask local image editing: turboCLI reuses ComfyUI models, runs 9B FLUX on 4 GB GPU — 3unjee · 2026-10-16
- Midjourney multi-sref weight mixing recipe for cinematic light & motion portraits — michaelrabone · 2026-10-16