Model reference · open weights
SANA-WM_bidirectional is an open-weight video model from Efficient-Large-Model. SANA-WM_bidirectional (BF16) weighs 102 GB; the smallest configuration that runs it is RTX PRO 6000 Blackwell 96 GB.
Summary of the Efficient-Large-Model/SANA-WM_bidirectional model card, 2026-10-01
What it is
| Released by | Efficient-Large-Model |
|---|---|
| Released | 2026-05-18 |
| VRAM | 102 GB for the weights |
What it runs on
| Card | Weights | Memory |
|---|---|---|
| RTX 3060 12 GB … H100 80 GB | does not fit | |
| RTX PRO 6000 Blackwell 96 GB | tight (encoders offloaded) | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | tight | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
SANA-WM is an efficient open-source world model trained natively for one-minute generation. The bidirectional checkpoint released here is a 2.6B-parameter image-to-video diffusion transformer that synthesises 720p, minute-scale videos with precise 6-DoF camera control, paired with the LTX-2 sink-bidirectional Euler refiner for high-fidelity decoding.
Four core designs drive the architecture:
Paper:
@article{zhu2026sanawm,
title = {{SANA-WM}: Efficient Minute-Scale World Modeling with Hybrid Linear Diffusion Transformer},
author = {Zhu, Haoyi and Liu, Haozhe and Zhao, Yuyang and Ye, Tian and Chen, Junsong and Yu, Jincheng and He, Tong and Han, Song and Xie, Enze},
journal = {arXiv preprint arXiv:2605.15178},
year = {2026},
}
| Component | Path in repo | Size |
|---|---|---|
| Sana DiT (Stage 1) | dit/sana_wm_1600m_720p.safetensors | 10 GB |
| LTX-2 VAE (diffusers) | vae/ | 2 GB |
| LTX-2 refiner (Stage 2) | refiner/refiner.safetensors | 41 GB |
| Gemma text encoder for the refiner | refiner/text_encoder/ | 46 GB |
| Inference config | config.yaml | — |
The Sana text encoder (gemma-2-2b-it) is not bundled here — it is
fetched on demand from the public Hugging Face mirror.
python inference_video_scripts/inference_sana_wm.py \
--image asset/sana_wm/demo_0.png \
--prompt asset/sana_wm/demo_0.txt \
--action "w-80,jw-40,w-40,lw-60,w-100" \
--translation_speed 0.055 \
--rotation_speed_deg 1.2 \
--num_frames 321 \
--output_dir results/demo
Weights are fetched from this repository on first use. Pass --no_refiner
to skip the LTX-2 refiner and decode Stage-1 latents with the Sana VAE
instead. To run fully offline, override any of --config / --model_path /
--refiner_checkpoint / --refiner_gemma_root with local paths.
| Argument | Format |
|---|---|
--image | RGB image (any PIL-readable format) — used as the first frame. |
--prompt | UTF-8 text file containing the conditioning prompt. |
--camera | NumPy .npy of shape (F, 4, 4) — per-frame camera-to-world matrices. |
--action | WASD/IJKL DSL, e.g. "w-80,jw-40,w-40,lw-60,w-100". We roll it out to a (F+1, 4, 4) trajectory. Mutually exclusive with --camera. |
--intrinsics | Optional. .npy of shape (3, 3), (F, 3, 3), or (4,). If omitted, we estimate intrinsics from --image with Pi3X and abort if the resulting FOV is outside [25°, 120°]. |
The output frame size is fixed at 704 x 1280; input images are
aspect-preserving resized + center-cropped to that resolution.
Released under the Apache 2.0 license. The bundled LTX-2 refiner and VAE inherit the LTX-2 upstream license.
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
How it works
Running it yourself
Rent a machine by the hour. ComfyUI is installed on it. Open ComfyUI through the tunnel and load the workflow from the model's card on Hugging Face; choose this model's file in its loader.
# on your rented machine (the ssh line is on its page in the console)
# get REPO FILE FOLDER: one file into /workspace/models/FOLDER, where ComfyUI loads it from
get() { hf download "$1" "$2" --local-dir /workspace/hf-files && mkdir -p "/workspace/models/$3" && mv "/workspace/hf-files/$2" "/workspace/models/$3/$4"; }
# the model (12.2 GB)
get Efficient-Large-Model/SANA-WM_bidirectional dit/sana_wm_1600m_720p.safetensors diffusion_models
get Efficient-Large-Model/SANA-WM_bidirectional vae/diffusion_pytorch_model.safetensors vae
start-comfyui# on your computer, in a second terminal: ComfyUI in your browser at http://localhost:8188
# HOST and PORT are your machine's, from its page in the console
ssh -L 8188:localhost:8188 dev@HOST -p PORT