SANA is an efficiency-oriented codebase for high-resolution image and video generation, providing complete training and inference pipelines. This repository contains code for SANA, SANA-1.5, SANA-Sprint, and SANA-Video. More details can be found in our 📚 documentation.

Join our Discord to engage in discussions with the community! If you have any questions, run into issues, or are interested in contributing, don't hesitate to reach out!

• (🔥 New) [2025/12/09] 🎬 LongSANA training and inference code are all released. Thanks to LongLive Team. Refer to: [Train] | [Test] | [Weight]
• (🔥 New) [2025/11/21] 🎬 LongSANA: 27FPS real-time minute-length video generation model is released. Thanks to LongLive. [Weight]
• (🔥 New) [2025/11/24] 🪶 Blog: how Causal Linear Attention unlocks infinite context for LLMs and long video generation.
• (🔥 New) [2025/11/9] 🎬 Introduction video shows how Block Causal Linear Attention and Causal Mix-FFN work?
• (🔥 New) [2025/11/6] 📺SANA-Video is merged into diffusers. How to use.
• (🔥 New) [2025/10/27] 📺SANA-Video is released. [README] | [Weights] support Text-to-Video, TextImage-to-Video.
• (🔥 New) [2025/10/13] 📺SANA-Video is coming, 1). a 5s Linear DiT Video model, and 2). real-time minute-length video generation (with LongLive). [paper] | [Page]
• (🔥 New) [2025/8/20] We release a new DC-AE-Lite for faster inference and smaller memory. [How to config] | [diffusers PR] | [Weight]
• (🔥 New) [2025/6/25] SANA-Sprint was accepted to ICCV'25 🏖️
• (🔥 New) [2025/6/4] SANA-Sprint ComfyUI Node is released [Example].
• (🔥 New) [2025/5/8] SANA-Sprint (One-step diffusion) diffusers training code is released [Guidance].

We introduce SANA, a series of efficient diffusion models for high-resolution image and video generation:

• SANA: Text-to-image generation up to 4K resolution, 20× smaller and 100× faster than Flux-12B.
• SANA-1.5: Efficient training-time and inference-time compute scaling for better quality.
• SANA-Sprint: One/few-step generation via sCM distillation, 0.1s per 1024px image on H100. | SANA-Video/LongSANA | Efficient video generation with Block Linear Attention / with LongLive|

Key Techniques:

• Linear Attention: Replace vanilla attention in DiT with linear attention for efficiency at high resolutions.
• DC-AE: 32× image compression (vs. traditional 8×) to reduce latent tokens.
• Decoder-only Text Encoder: Modern decoder-only LLM with in-context learning for better text-image alignment.
• Block Causal Linear Attention & Causal Mix-FFN: Efficient attention and feedforward for long video generation.
• Flow-DPM-Solver: Reduce sampling steps with efficient training and sampling.
• sCM Distillation: One/few-step generation with continuous-time consistency distillation.

In summary, SANA is a fully open-source framework integrating efficient training, fast inference, and flexible deployment for both image and video generation. Deployable on laptop GPUs with < 8GB VRAM via 4-bit quantization.

git clone https://github.com/NVlabs/Sana.git
cd Sana && ./environment_setup.sh sana

import torch
from diffusers import SanaPipeline

pipe = SanaPipeline.from_pretrained(
    "Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers",
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

pipe.vae.to(torch.bfloat16)
pipe.text_encoder.to(torch.bfloat16)

prompt = 'a cyberpunk cat with a neon sign that says "Sana"'
image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    guidance_scale=4.5,
    num_inference_steps=20,
    generator=torch.Generator(device="cuda").manual_seed(42),
)[0]

image[0].save("sana.png")

• 📚 Full Documentation
• Installation Guide
• Model Zoo
• Sana Inference & Training
• SANA-Sprint
• SANA-Video
• LongSANA
• ControlNet
• LoRA / DreamBooth
• Quantization (4bit / 8bit)
• ComfyUI

We will try our best to achieve

• [✅] Training code
• [✅] Inference code
• [✅] Model zoo
• [✅] ComfyUI Nodes(SANA, SANA-1.5, SANA-Sprint)
• [✅] DC-AE Diffusers
• [✅] Sana merged in Diffusers(huggingface/diffusers#9982)
• [✅] LoRA training by @paul(diffusers: https://github.com/ huggingface/diffusers/pull/10234)
• [✅] 2K/4K resolution models.(Thanks @SUPIR to provide a 4K super-resolution model)
• [✅] 8bit / 4bit Laptop development
• [✅] ControlNet (train & inference & models)
• [✅] FSDP Training
• [✅] SANA-1.5 (Larger model size / Inference Scaling)
• [✅] SANA-Sprint: Few-step generator
• [✅] Faster DCAE-Lite weight
• [✅] Better re-construction F32/F64 VAEs.
• [✅] SANA-Video: Linear DiT Video model, and real-time minute-length video generation
• [🚀] See you in the future

Thanks to the following open-sourced projects:

Thanks to the following open-sourced codebase for their wonderful work and codebase!

• PixArt-α
• PixArt-Σ
• diffusers
• Efficient-ViT
• ComfyUI_ExtraModels
• SVDQuant and Nunchaku
• Open-Sora
• Wan
• LongLive

Thanks Paper2Video for generating Jeason presenting SANA😊. Refer to Paper2Video for more details.

Thanks goes to these wonderful contributors:

@misc{xie2024sana,
      title={Sana: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer},
      author={Enze Xie and Junsong Chen and Junyu Chen and Han Cai and Haotian Tang and Yujun Lin and Zhekai Zhang and Muyang Li and Ligeng Zhu and Yao Lu and Song Han},
      year={2024},
      eprint={2410.10629},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2410.10629},
    }
@misc{xie2025sana,
      title={SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer},
      author={Xie, Enze and Chen, Junsong and Zhao, Yuyang and Yu, Jincheng and Zhu, Ligeng and Lin, Yujun and Zhang, Zhekai and Li, Muyang and Chen, Junyu and Cai, Han and others},
      year={2025},
      eprint={2501.18427},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2501.18427},
    }
@misc{chen2025sanasprint,
      title={SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation},
      author={Junsong Chen and Shuchen Xue and Yuyang Zhao and Jincheng Yu and Sayak Paul and Junyu Chen and Han Cai and Song Han and Enze Xie},
      year={2025},
      eprint={2503.09641},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.09641},
    }
@misc{chen2025sana,
      title={SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer},
      author={Chen, Junsong and Zhao, Yuyang and Yu, Jincheng and Chu, Ruihang and Chen, Junyu and Yang, Shuai and Wang, Xianbang and Pan, Yicheng and Zhou, Daquan and Ling, Huan and others},
      year={2025},
      eprint={2509.24695},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.24695},
    }