buoyancy99/diffusion-forcing

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1288 stars · 71 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official research code for the NeurIPS paper 'Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion', implementing a method that combines next-token prediction with full-sequence diffusion models. It supports video generation, planning, and robotics experiments with both RNN (paper branch) and temporal attention (main branch) implementations.

Use cases

• reproduce results from the Diffusion Forcing paper
• generate videos with diffusion models
• train sequence models that combine autoregressive prediction with diffusion
• run planning experiments in maze environments
• apply diffusion forcing to robotics control tasks
• experiment with per-token diffusion noise levels

When to choose

• you want to implement or extend the Diffusion Forcing method from the paper
• you need a research codebase for hybrid autoregressive-diffusion sequence modeling
• you are working on video generation or planning/robotics research based on this technique

When to avoid

• you only want state-of-the-art video generation - use Diffusion Forcing v2 (diffusion-forcing-transformer) instead
• you need a production-ready library with stable APIs and support
• you want a plug-and-play tool rather than research code requiring conda setup and wandb configuration

Facets

library · maturity active

machine-learning deep-learning llm-training video-processing simulation machine-learning deep-learning artificial-intelligence robotics python diffusion-models sequence-modeling research-code video-generation planning neurips-paper pytorch video linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="buoyancy99/diffusion-forcing")