NVIDIA's Flex-Forcing Lets One Model Stream and Render Video at 48 FPS
NVIDIA's Flex-Forcing lets a single video diffusion model switch between fast streaming and high-quality generation at inference time, earning an ICML 2026 spotlight.
- Single model, all modes: Flex-Forcing lets one video diffusion model run as bidirectional, autoregressive, or anything in between at inference time.
- ICML 2026 spotlight: The paper earned a spotlight designation at ICML 2026, one of the conference's top recognition tiers.
- Speed + quality wins: Best config scores 85.07 VBench at 25.8 FPS; fastest config hits 29.4 FPS — both beating Self-Forcing baseline.
- Key innovation — K-Projection: A lightweight timestep-conditioned layer aligns noisy and clean key states in attention, enabling stable mixed-mode inference.
- Any-order video editing: The model can re-edit arbitrary middle segments of a video without regenerating the whole clip, using both past and future frames as context.
- No public weights yet: Built on open Wan2.1-T2V-1.3B; training details are published but model checkpoints have not been released.
Video generation has long forced a hard choice: do you want quality or speed? Bidirectional diffusion models look at every frame at once and produce coherent, visually rich clips, but they are slow and expensive to run. Autoregressive models generate frame by frame, enabling real-time streaming and arbitrarily long videos, but they accumulate errors over time and lose track of what happened earlier in the clip. NVIDIA Research's new Flex-Forcing argues you should not have to choose at all.
One model, every mode
Flex-Forcing is a unified training and inference framework that enables a single video diffusion model to seamlessly operate under both bidirectional and autoregressive generation regimes. The key insight is that these two paradigms are not fundamentally different architectures , they are just two extreme points on a continuous spectrum of how much context each frame is allowed to see.
The core idea is a flexible chunking mechanism jointly defined over the temporal axis and denoising steps. This design allows the model to perform flexible chunking according to different device budgets, perform bidirectional inference across chunks for global structure planning while generating frames autoregressively within each chunk for efficient synthesis, and perform any-order, any-timestep autoregressive generation without the strict causal constraint.
Think of it like this: in fully autoregressive mode, every frame is its own chunk of size one. In fully bidirectional mode, all frames are one giant chunk. Flex-Forcing lets you pick anything in between , and crucially, the same trained model handles all of it.
The two problems it had to solve
Unifying these paradigms in a single model requires solving two non-trivial technical problems.
The first is training the model to handle both causal (left-to-right) and non-causal (full-context) attention simultaneously. Flex-Forcing introduces a unified training and inference framework that enables the model to seamlessly operate under both regimes. During training, the team randomly samples different chunk sizes at each step, exposing the model to the full spectrum of attention patterns from strictly causal to fully bidirectional.