ICLR 2025 Oral | ICML 2025 | ICCV 2025 Highlight | ICLR 2026 Oral
**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](https://nvlabs.github.io/Sana/), [SANA-1.5](https://nvlabs.github.io/Sana/Sana-1.5/), [SANA-Sprint](https://nvlabs.github.io/Sana/Sprint/), [SANA-Video](https://nvlabs.github.io/Sana/Video/), [SANA-WM](https://nvlabs.github.io/Sana/WM/), and [Sol-RL](https://nvlabs.github.io/Sana/Sol-RL/). More details can be found in our [π documentation](https://nvlabs.github.io/Sana/docs/).
Join our [Discord](https://discord.gg/rde6eaE5Ta) 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!
## News
- π₯ [2026/05] π **SANA-WM: 2.6B Controllable World Model** is released! Supports 720p, 1-min video generation with 6-DoF camera control. A new baseline for World Modeling and Embodied AI. See [Project](https://nvlabs.github.io/Sana/WM/) | [Paper](https://huggingface.co/papers/2605.15178).
- π₯ [2026/04] β‘ **Sol-RL: NVFP4 Rollout, BF16 Training RL** is available! All training recipes for **SANA**, **FLUX.1**, and **SD3.5-L**, together with bundled post-training datasets, are released. See [Sol-RL doc](https://nvlabs.github.io/Sana/docs/sol_rl/) | [Page](https://nvlabs.github.io/Sana/Sol-RL/) | [Paper](https://arxiv.org/abs/2604.06916).
- π₯ [2026/03] πΊ **SANA-Video 720p model with LTX-VAE** is released. Use it with LTX2 Refiner to upscale the videos to 2K resolution! See [Model Zoo](https://nvlabs.github.io/Sana/docs/model_zoo/#sana-video), [SANA-Video doc](https://nvlabs.github.io/Sana/docs/sana_video/) and [Blog about refiner](https://nvlabs.github.io/Sana/Video/bet-small-win-big/blog.html).
- π₯ [2026/03] πͺ **Post Training Infra: SANA Γ Cosmos-RL** β We partner with [Cosmos-RL](https://github.com/nvidia-cosmos/cosmos-rl) to provide a complete RL infrastructure for SANA. You can now post-train (SFT/RL) SANA-Image and SANA-Video with state-of-the-art algorithms (e.g. Diffusion-NFT, Flow-GRPO), preset configs, reward services, and flexible datasets. See [SANA on Cosmos-RL](https://github.com/nvidia-cosmos/cosmos-rl/blob/main/examples/sana.md) and our [Cosmos-RL integration doc](https://nvlabs.github.io/Sana/docs/sana_cosmos_rl/).
- π₯ [2026/02] π **SANA is now supported in [SGLang](https://github.com/sgl-project/sglang)!** High-performance serving with OpenAI-compatible API. [[Guidance]](https://nvlabs.github.io/Sana/docs/sglang/)
- π₯ [2026/01/26] **SANA-Video is accepted as Oral by ICLR-2026.** πππ
- π₯ [2025/12/09] π¬ [LongSANA](https://nvlabs.github.io/Sana/docs/longsana/): 27FPS real-time minute-length video generation model, training and inference code are all released. Thanks to [LongLive Team](https://github.com/NVlabs/LongLive). Refer to: [[Train]](https://nvlabs.github.io/Sana/docs/longsana/#how-to-train) | [[Test]](https://nvlabs.github.io/Sana/docs/longsana/#how-to-inference) | [[Weight]](https://nvlabs.github.io/Sana/docs/model_zoo/#sana-video)
- π₯ [2025/11/24] πͺΆ [Blog](https://hanlab.mit.edu/blog/infinite-context-length-with-global-but-constant-attention-memory): how Causal Linear Attention unlocks infinite context for LLMs and long video generation.
- π₯ [2025/11/9] π¬ [Introduction video](https://www.youtube.com/watch?v=ztdkfIMkdJ4) shows how Block Causal Linear Attention and Causal Mix-FFN work?
- π₯ [2025/11/6] πΊ**SANA-Video** is merged into [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana_video). [How to use](https://nvlabs.github.io/Sana/docs/sana_video/#1-how-to-use-sana-video-pipelines-in-diffusers).
- π₯ [2025/10/27] πΊ**SANA-Video** is released. [[README]](https://nvlabs.github.io/Sana/docs/sana_video/) | [[Weights]](https://nvlabs.github.io/Sana/docs/model_zoo/#sana-video) support Text-to-Video, TextImage-to-Video.
- π₯ [2025/10/13] πΊ**SANA-Video** is coming, 1). a 5s Linear DiT Video model, and 2). real-time minute-length video generation (with [LongLive](https://github.com/NVlabs/LongLive)). [[paper]](https://www.arxiv.org/pdf/2509.24695) | [[Page]](https://nvlabs.github.io/Sana/Video/)
Click to show all updates
- β
[2025/8/20] We release a new DC-AE-Lite for faster inference and smaller memory. [[How to config]](https://github.com/NVlabs/Sana/blob/main/configs/sana_sprint_config/1024ms/SanaSprint_1600M_1024px_allqknorm_bf16_scm_ladd_dc_ae_lite.yaml#L52) | [[diffusers PR]](https://github.com/huggingface/diffusers/pull/12169) | [[Weight]](https://huggingface.co/mit-han-lab/dc-ae-lite-f32c32-sana-1.1-diffusers)
- β
[2025/6/25] [SANA-Sprint](https://nvlabs.github.io/Sana/Sprint/) was accepted to ICCV'25 ποΈ
- β
[2025/6/4] SANA-Sprint [ComfyUI Node](https://github.com/lawrence-cj/ComfyUI_ExtraModels) is released [[Example]](docs/ComfyUI/SANA-Sprint.json).
- β
[2025/5/8] SANA-Sprint (One-step diffusion) diffusers training code is released [[Guidance]](https://github.com/huggingface/diffusers/blob/main/examples/research_projects/sana/README.md).
- β
[2025/5/4] **SANA-1.5 (Inference-time scaling) is accepted by ICML-2025.** πππ
- β
[2025/3/22] π₯**SANA-Sprint demo is hosted on Huggingface, try it!** π [[Demo Link]](https://huggingface.co/spaces/Efficient-Large-Model/SanaSprint)
- β
[2025/3/22] π₯**SANA-1.5 is supported in ComfyUI!** π: [ComfyUI Guidance](https://nvlabs.github.io/Sana/docs/ComfyUI/comfyui/) | [ComfyUI Work Flow SANA-1.5 4.8B](https://nvlabs.github.io/Sana/docs/ComfyUI/SANA-1.5_FlowEuler.json)
- β
[2025/3/22] π₯**SANA-Sprint code & weights are released!** π Include: [Training & Inference](https://nvlabs.github.io/Sana/docs/sana_sprint/) code and [Weights](https://nvlabs.github.io/Sana/docs/model_zoo/#sana-sprint) / [HF](https://huggingface.co/collections/Efficient-Large-Model/sana-sprint) are all released. [[Guidance]](https://nvlabs.github.io/Sana/docs/sana_sprint/)
- β
[2025/3/21] πSana + **Inference Scaling** is released. [[Guidance]](https://nvlabs.github.io/Sana/docs/inference_scaling/)
- β
[2025/3/16] π₯**SANA-1.5 code & weights are released!** π Include: [DDP/FSDP](https://nvlabs.github.io/Sana/docs/sana/#training) | [TAR file WebDataset](https://nvlabs.github.io/Sana/docs/sana/#multi-scale-webdataset) | [Multi-Scale](https://nvlabs.github.io/Sana/docs/sana/#training-with-fsdp) Training code and [Weights](https://nvlabs.github.io/Sana/docs/model_zoo/#sana-15) | [HF](https://huggingface.co/collections/Efficient-Large-Model/sana-15) are all released.
- β
[2025/3/14] π**SANA-Sprint is coming out!** π A new one/few-step generator of Sana. 0.1s per 1024px image on H100, 0.3s on RTX 4090. Find out more details: [[Page]](https://nvlabs.github.io/Sana/Sprint/) | [[Arxiv]](https://arxiv.org/abs/2503.09641). Code is coming very soon along with `diffusers`
- β
[2025/2/10] πSana + ControlNet is released. [[Guidance]](https://nvlabs.github.io/Sana/docs/sana_controlnet/) | [[Model]](https://nvlabs.github.io/Sana/docs/model_zoo/#sana) | [[Demo]](https://nv-sana.mit.edu/ctrlnet/)
- β
[2025/1/30] Release CAME-8bit optimizer code. Saving more GPU memory during training. [[How to config]](https://github.com/NVlabs/Sana/blob/main/configs/sana_config/1024ms/Sana_1600M_img1024_CAME8bit.yaml#L86)
- β
[2025/1/29] π π π**SANA 1.5 is out! Figure out how to do efficient training & inference scaling!** π[[Tech Report]](https://arxiv.org/abs/2501.18427)
- β
[2025/1/24] 4bit-Sana is released, powered by [SVDQuant and Nunchaku](https://github.com/mit-han-lab/nunchaku) inference engine. Now run your Sana within **8GB** GPU VRAM [[Guidance]](https://nvlabs.github.io/Sana/docs/4bit_sana/) [[Demo]](https://svdquant.mit.edu/) [[Model]](https://nvlabs.github.io/Sana/docs/model_zoo/#sana)
- β
[2025/1/24] DCAE-1.1 is released, better reconstruction quality. [[Model]](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.1) [[diffusers]](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers)
- β
[2025/1/23] **Sana is accepted as Oral by ICLR-2025.** πππ
- β
[2025/1/12] DC-AE tiling makes Sana-4K inferences 4096x4096px images within 22GB GPU memory. With model offload and 8bit/4bit quantize. The 4K Sana run within **8GB** GPU VRAM. [[Guidance]](https://nvlabs.github.io/Sana/docs/model_zoo/#3-2k-4k-models)
- β
[2025/1/11] Sana code-base license changed to Apache 2.0.
- β
[2025/1/10] Inference Sana with 8bit quantization.[[Guidance]](https://nvlabs.github.io/Sana/docs/8bit_sana/#quantization)
- β
[2025/1/8] 4K resolution [Sana models](https://nvlabs.github.io/Sana/docs/model_zoo/#sana) is supported in [Sana-ComfyUI](https://github.com/lawrence-cj/ComfyUI_ExtraModels) and [work flow](https://nvlabs.github.io/Sana/docs/ComfyUI/Sana_FlowEuler_4K.json) is also prepared. [[4K guidance]](https://nvlabs.github.io/Sana/docs/ComfyUI/comfyui/#a-sample-workflow-for-sana-4096x4096-image-18gb-gpu-is-needed)
- β
[2025/1/8] 1.6B 4K resolution [Sana models](https://nvlabs.github.io/Sana/docs/model_zoo/#sana) are released: [[BF16 pth]](https://huggingface.co/Efficient-Large-Model/Sana_1600M_4Kpx_BF16) or [[BF16 diffusers]](https://huggingface.co/Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers). π Get your 4096x4096 resolution images within 20 seconds! Find more samples in [Sana page](https://nvlabs.github.io/Sana/). Thanks [SUPIR](https://github.com/Fanghua-Yu/SUPIR) for their wonderful work and support.
- β
[2025/1/2] Bug in the `diffusers` pipeline is solved. [Solved PR](https://github.com/huggingface/diffusers/pull/10431)
- β
[2025/1/2] 2K resolution [Sana models](asset/docs/model_zoo.md) is supported in [Sana-ComfyUI](https://github.com/lawrence-cj/ComfyUI_ExtraModels) and [work flow](asset/docs/ComfyUI/Sana_FlowEuler_2K.json) is also prepared.
- β
[2024/12] 1.6B 2K resolution [Sana models](asset/docs/model_zoo.md) are released: [[BF16 pth]](https://huggingface.co/Efficient-Large-Model/Sana_1600M_2Kpx_BF16) or [[BF16 diffusers]](https://huggingface.co/Efficient-Large-Model/Sana_1600M_2Kpx_BF16_diffusers). π Get your 2K resolution images within 4 seconds! Find more samples in [Sana page](https://nvlabs.github.io/Sana/). Thanks [SUPIR](https://github.com/Fanghua-Yu/SUPIR) for their wonderful work and support.
- β
[2024/12] `diffusers` supports Sana-LoRA fine-tuning! Sana-LoRA's training and convergence speed is super fast. [[Guidance]](https://nvlabs.github.io/Sana/docs/sana_lora_dreambooth/) or [[diffusers docs]](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sana.md).
- β
[2024/12] `diffusers` has Sana! [All Sana models in diffusers safetensors](https://huggingface.co/collections/Efficient-Large-Model/sana) are released and diffusers pipeline `SanaPipeline`, `SanaPAGPipeline`, `DPMSolverMultistepScheduler(with FlowMatching)` are all supported now. We prepare a [Model Card](https://nvlabs.github.io/Sana/docs/model_zoo/#sana) for you to choose.
- β
[2024/12] 1.6B BF16 [Sana model](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_BF16) is released for stable fine-tuning.
- β
[2024/12] We release the [ComfyUI node](https://github.com/lawrence-cj/ComfyUI_ExtraModels) for Sana. [[Guidance]](https://nvlabs.github.io/Sana/docs/ComfyUI/comfyui/)
- β
[2024/11] All multi-linguistic (Emoji & Chinese & English) SFT models are released: [1.6B-512px](https://huggingface.co/Efficient-Large-Model/Sana_1600M_512px_MultiLing), [1.6B-1024px](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_MultiLing), [600M-512px](https://huggingface.co/Efficient-Large-Model/Sana_600M_512px), [600M-1024px](https://huggingface.co/Efficient-Large-Model/Sana_600M_1024px). The metric performance is shown [here](#performance)
- β
[2024/11] Sana Replicate API is launching at [Sana-API](https://replicate.com/chenxwh/sana).
- β
[2024/11] 1.6B [Sana models](https://huggingface.co/collections/Efficient-Large-Model/sana) are released.
- β
[2024/11] Training & Inference & Metrics code are released.
- β
[2024/11] Working on [`diffusers`](https://github.com/huggingface/diffusers/pull/9982).
- [2024/10] [Demo](https://nv-sana.mit.edu/) is released.
- [2024/10] [DC-AE Code](https://github.com/mit-han-lab/efficientvit/blob/master/applications/dc_ae/README.md) and [weights](https://huggingface.co/collections/mit-han-lab/dc-ae) are released!
- [2024/10] [Paper](https://arxiv.org/abs/2410.10629) is on Arxiv!
## π‘ Introduction
We introduce **SANA**, a series of efficient diffusion models for high-resolution image and video generation:
- **[SANA](https://nvlabs.github.io/Sana/)**: Text-to-image generation up to 4K resolution, **20Γ smaller and 100Γ faster** than Flux-12B.
- **[SANA-1.5](https://nvlabs.github.io/Sana/Sana-1.5/)**: Efficient training-time and inference-time compute scaling for better quality.
- **[SANA-Sprint](https://nvlabs.github.io/Sana/Sprint/)**: One/few-step generation via sCM distillation, **0.1s per 1024px image** on H100.
- **[SANA-Video/LongSANA](https://nvlabs.github.io/Sana/Video/)**: Efficient video generation with Block Linear Attention / with [LongLive](https://github.com/NVlabs/LongLive).
- **[Sol-RL](https://nvlabs.github.io/Sana/Sol-RL/)**: NVFP4 Rollout, BF16 Training RL achieves **4.64Γ faster convergence**.
- **[SANA-WM](https://nvlabs.github.io/Sana/WM/)**: 2.6B parameter controllable world model, generating 720p, 1-minute video worlds with 6-DoF camera control.
**Key Techniques:**
- **Linear Attention**: Replace vanilla attention in DiT with linear attention for efficiency at high resolutions.
- **[DC-AE](https://hanlab.mit.edu/projects/dc-ae)**: 32Γ image compression (vs. traditional 8Γ) 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.
- **Sol-RL**: Low precision(NVFP4) rollout selection, high precesion(BF16) optimization for faster RL training.
- **Controllable World Modeling**: Efficient long-context modeling and camera trajectory control for consistent world generation.
**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.
## Quick Start
```bash
git clone https://github.com/NVlabs/Sana.git
cd Sana && ./environment_setup.sh sana
```
### Inference with 𧨠diffusers
```python
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")
```
> [!TIP]
> Upgrade your `diffusers>=0.32.0` to use `SanaPipeline`. More details can be found in [π Docs](https://nvlabs.github.io/Sana/docs/).
## Getting Started
- [π **Full Documentation**](https://nvlabs.github.io/Sana/docs/)
- [Installation Guide](https://nvlabs.github.io/Sana/docs/installation/)
- [Model Zoo](https://nvlabs.github.io/Sana/docs/model_zoo/)
- [Sana Inference & Training](https://nvlabs.github.io/Sana/docs/sana/)
- [SANA-Sprint](https://nvlabs.github.io/Sana/docs/sana_sprint/)
- [SANA-Video](https://nvlabs.github.io/Sana/docs/sana_video/)
- [LongSANA](https://nvlabs.github.io/Sana/docs/longsana/)
- [SANA-WM(coming soon)](https://nvlabs.github.io/Sana/docs/world-model/)
- [ControlNet](https://nvlabs.github.io/Sana/docs/sana_controlnet/)
- [LoRA / DreamBooth](https://nvlabs.github.io/Sana/docs/sana_lora_dreambooth/)
- [Sol-RL Post-Training](https://nvlabs.github.io/Sana/docs/sol_rl/)
- [Quantization (4bit / 8bit)](https://nvlabs.github.io/Sana/docs/4bit_sana/)
- [ComfyUI](https://nvlabs.github.io/Sana/docs/ComfyUI/comfyui/)
- [SGLang](https://nvlabs.github.io/Sana/docs/sglang/)
## Performance
### Image Generation (1024px)
| Methods (1024x1024) | Throughput (samples/s) | Latency (s) | Params (B) | Speedup | FID π | CLIP π | GenEval π | DPG π |
|--------------------------------------------------------------------------------------------------|------------------------|-------------|------------|---------|-------------|--------------|-------------|---------------|
| FLUX-dev | 0.04 | 23.0 | 12.0 | 1.0Γ | 10.15 | 27.47 | 0.67 | 84.0 |
| **Sana-0.6B** | 1.7 | 0.9 | 0.6 | 39.5Γ | _5.81_ | 28.36 | 0.64 | 83.6 |
| **[Sana-0.6B](https://huggingface.co/Efficient-Large-Model/Sana_600M_1024px)** | 1.7 | 0.9 | 0.6 | 39.5Γ | **5.61** | 28.80 | 0.68 | _84.2_ |
| **[Sana-1.6B](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_MultiLing)** | 1.0 | 1.2 | 1.6 | 23.3Γ | 5.92 | _28.94_ | _0.69_ | 84.5 |
| **[Sana-1.5 1.6B](https://huggingface.co/Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers)** | 1.0 | 1.2 | 1.6 | 23.3Γ | 5.70 | 29.12 | **0.82** | 84.5 |
| **[Sana-1.5 4.8B](https://huggingface.co/Efficient-Large-Model/SANA1.5_4.8B_1024px_diffusers)** | 0.26 | 4.2 | 4.8 | 6.5Γ | 5.99 | **29.23** | 0.81 | **84.7** |
### Video Generation (VBench 720p)
| Models | Latency (s) | Params (B) | VBench Total β | Quality β | Semantic β |
|--------|-------------|------------|----------------|-----------|------------|
| Wan-2.1-14B | 1897 | 14 | 83.73 | 85.77 | 75.58 |
| Wan-2.1-1.3B | 400 | 1.3 | 83.38 | 85.67 | 74.22 |
| **SANA-Video-2B** | **36** | **2** | **84.05** | 84.63 | **81.73** |
# πͺTo-Do List
We will try our best to achieve
- [β
] Training code
- [β
] Inference code
- [β
] Model zoo
- [β
] [ComfyUI Nodes](https://github.com/lawrence-cj/ComfyUI_ExtraModels)(SANA, SANA-1.5,
SANA-Sprint)
- [β
] DC-AE Diffusers
- [β
] Sana merged in Diffusers(https://github.com/huggingface/diffusers/pull/9982)
- [β
] LoRA training by [@paul](https://github.com/sayakpaul)(`diffusers`: https://github.com/
huggingface/diffusers/pull/10234)
- [β
] 2K/4K resolution models.(Thanks [@SUPIR](https://github.com/Fanghua-Yu/SUPIR) to
provide a 4K super-resolution model)
- [β
] 8bit / 4bit Laptop development
- [β
] ControlNet (train & inference & models)
- [β
] FSDP Training
- [β
] SANA-1.5 (Larger model size / Inference Scaling)
- [β
] SANA-Sprint: Few-step generator
- [β
] Faster DCAE-Lite [weight](https://huggingface.co/dc-ai/dc-ae-lite-f32c32-diffusers)
- [β
] Better re-construction F32/F64 [VAEs](https://github.com/dc-ai-projects/DC-Gen)
- [β
] SANA-Video: Linear DiT Video model, and real-time minute-length video generation
- [β
] RL Post-training: collaborate with [Cosmos-RL](https://github.com/nvidia-cosmos/cosmos-rl)
- [] SANA World Model
- [] SANA Streaming Video-to-Video Editing
- [π] See you in the future
## π€ Acknowledgements
Thanks to the following open-sourced projects:
**Thanks to the following open-sourced codebase for their wonderful work and codebase!**
- [PixArt-Ξ±](https://github.com/PixArt-alpha/PixArt-alpha)
- [PixArt-Ξ£](https://github.com/PixArt-alpha/PixArt-sigma)
- [diffusers](https://github.com/huggingface/diffusers)
- [Efficient-ViT](https://github.com/mit-han-lab/efficientvit)
- [ComfyUI_ExtraModels](https://github.com/city96/ComfyUI_ExtraModels)
- [SVDQuant and Nunchaku](https://github.com/mit-han-lab/nunchaku)
- [Open-Sora](https://github.com/hpcaitech/Open-Sora)
- [Wan](https://github.com/Wan-Video/Wan2.1)
- [LongLive](https://github.com/NVlabs/LongLive)
- [Cosmos-RL](https://github.com/nvidia-cosmos/cosmos-rl)
Thanks [Paper2Video](https://showlab.github.io/Paper2Video/) for generating Jeason presenting SANAπ. Refer to [Paper2Video](https://showlab.github.io/Paper2Video/) for more details.
## Contribution
Thanks go to these wonderful contributors:
## π Star History
[](https://www.star-history.com/#NVlabs/sana&Date)
# π BibTeX
```bibtex
@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},
}
```
Click to expand all BibTeX citations
```bibtex
@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 rectangle 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 graves 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{chen2025sanavideo,
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},
}
@misc{li2026fp4,
title={FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling},
author={Li, Yitong and Chen, Junsong and Xue, Shuchen and Zeren, Pengcuo and Fu, Siyuan and Yang, Dinghao and Tang, Yangyang and Bai, Junjie and Luo, Ping and Han, Song and others},
year={2026}
eprint={2604.06916},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2604.06916},
}
@misc{zhu2026sanawm,
title={SANA-WM: Efficient Minute-Scale World Modeling with Hybrid Linear Diffusion Transformer},
author={Haoyi Zhu and Haozhe Liu and Yuyang Zhao and Tian Ye and Junsong Chen and Jincheng Yu and Tong He and Song Han and Enze Xie},
year={2026},
eprint={2605.15178},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2605.15178},
}
```