NVlabs/Sana

Health v2 · maintenance only

Adoption not part of the score

8833 stars · 710 forks observed · 2026-08-28

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

SANA is an efficiency-oriented PyTorch codebase for high-resolution text-to-image and text-to-video generation built on Linear Diffusion Transformers. It provides complete training and inference pipelines for models including Sana, Sana-Sprint (few-step distillation), Sana-Video, and LongSana real-time video generation.

Use cases

• generate 4K images from text prompts
• efficient text-to-image generation on consumer GPUs with limited VRAM
• one-step or few-step image generation with distilled models
• generate 720p video from text or image prompts
• train or fine-tune diffusion transformer models
• run real-time streaming video generation
• post-train image and video models with reinforcement learning

When to choose

• you need high-resolution image or video generation with limited compute or VRAM
• you want fast few-step inference via consistency distillation
• you need a full open training and inference pipeline for diffusion transformers
• you want to experiment with linear attention for generative models

When to avoid

• you need a simple plug-and-play image generator without GPU setup
• you require non-Apache-licensed or fully closed model weights
• your project depends on standard softmax attention architectures incompatible with linear DiT

Facets

library · maturity active

machine-learning deep-learning image-processing video-processing llm-training deep-learning image-processing artificial-intelligence gpu-computing python text-to-image text-to-video diffusion-transformer linear-attention consistency-distillation pytorch high-resolution-generation world-models video gpu linux docker

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