guandeh17/Self-Forcing

Health v2 · maintenance only

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3551 stars · 286 forks observed · 2026-10-16

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official implementation of Self Forcing, a training method for autoregressive video diffusion models that simulates inference during training to close the train-test gap. It enables real-time streaming text-to-video generation on a single RTX 4090 with quality matching state-of-the-art diffusion models.

Use cases

• generate video from text prompts in real time
• stream video generation on a single consumer GPU
• train autoregressive video diffusion models
• run a text-to-video demo locally
• experiment with diffusion model training techniques
• generate videos chunk-by-chunk with KV caching

When to choose

• you need real-time or streaming video generation on a single 24GB GPU
• you want to reproduce or build on the Self Forcing research
• you need autoregressive chunk-wise video generation with low latency

When to avoid

• you need production-grade video generation without GPU hardware
• you want a polished end-user video generation product rather than research code
• you lack a Linux machine with an NVIDIA GPU and 64GB RAM

Facets

library · maturity active

machine-learning deep-learning video-processing llm-inference gpu-computing deep-learning artificial-intelligence image-processing python video-diffusion autoregressive-generation text-to-video streaming-generation diffusion-models research-code neurips-2025 video linux gpu

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