SANA-Video 2.0 5B 720p
SANA-Video 2.0 is an efficient diffusion transformer for high-resolution video generation. This release provides the 5B-class checkpoint jointly post-trained for text-to-video (T2V) and text-image-to-video (TI2V) generation at 720p for about eight seconds.
The model combines gated bidirectional linear-attention layers with periodic
dense softmax-attention anchors and shared Attention Residual aggregation. It
uses Gemma 2 2B IT for text conditioning and the LTX 2.3 VAE contract with 128
latent channels and (8, 32, 32) temporal/spatial compression.
Model details
| Property | Value |
|---|---|
| Architecture | SanaVideo2_5B |
| Parameters | 4,466,980,960 trainable model parameters (4.47B) |
| Transformer | 32 layers, hidden size 2,560 |
| Attention | 75% gated linear attention, 25% dense softmax anchors |
| Attention Residuals | Shared, timestep-independent aggregation every 8 layers |
| Tasks | Text-to-video and text-image-to-video |
| Output bucket | 736 × 1280, 193 frames, 24 FPS (about 8 seconds) |
| Text encoder | google/gemma-2-2b-it |
| VAE | LTX 2.3, 128 latent channels, stride (8, 32, 32) |
| Recommended inference | BF16, CFG 8, flow shift 12, 50 steps, motion score 20 |
| License | Apache 2.0 |
The checkpoint is an inference artifact containing only the merged model
state_dict. It does not contain optimizer, scheduler, training-state, or
standalone LoRA tensors. The EMA base weights and ReFL post-training adapter
were merged before release. Stored tensors retain their merged source dtypes;
the official inference entry point casts the transformer to BF16.
Files
checkpoints/SANA_Video_2.0_5B_720p.pth: merged transformer checkpointconfig.yaml: matching SANA training and inference configurationLICENSE: Apache License 2.0
Verified release example
This sample was generated from the public checkpoint with seed 4. The encoded result is 1280 × 736, 193 frames, 24 FPS, and 8.04 seconds long.
Prompt: In a cozy, vintage room adorned with floral wallpaper, a cartoon rooster sits comfortably in a floral-patterned armchair, sipping from a bottle of beer. The rooster, with its vibrant red comb and wattle, displays a range of expressions—smiling, nodding, and opening its beak wide in a cheerful manner. The setting includes wooden furniture and another beer bottle on the table, adding to the relaxed atmosphere. The camera captures the rooster from a close-up angle, emphasizing its animated movements and lively demeanor.
Inference
Support for this checkpoint is included in the main branch of NVlabs/Sana:
git clone https://github.com/NVlabs/Sana.git
cd Sana
bash environment_setup.sh sana
conda activate sana
Place the Diffusers-format LTX 2.3 VAE at
output/pretrained_models/LTX-2.3-Diffusers/, or update
vae.vae_pretrained in config.yaml.
Text-to-video
The command below is the exact command used for the verified release example:
bash inference_video_scripts/inference_sana_video.sh \
--np 1 \
--config configs/sana_video2/SanaVideo2_5B_720p.yaml \
--model_path hf://Efficient-Large-Model/SANA-Video_2.0_5B_720p/checkpoints/SANA_Video_2.0_5B_720p.pth \
--txt_file=asset/samples/sana_video2_5b_720p_demo.txt \
--cfg_scale 8 \
--flow_shift 12 \
--step 50 \
--fps 24 \
--motion_score 20 \
--seed 4 \
--work_dir output/sana_video2_t2v_720p_demo
Text-image-to-video
Each line in asset/samples/sample_i2v.txt contains a prompt and an input-image
path separated by <image>.
bash inference_video_scripts/inference_sana_video.sh \
--np 1 \
--config configs/sana_video2/SanaVideo2_5B_720p.yaml \
--model_path hf://Efficient-Large-Model/SANA-Video_2.0_5B_720p/checkpoints/SANA_Video_2.0_5B_720p.pth \
--txt_file=asset/samples/sample_i2v.txt \
--task=ltx \
--cfg_scale 8 \
--flow_shift 12 \
--step 50 \
--fps 24 \
--motion_score 20 \
--work_dir output/sana_video2_ti2v_720p
The default 720p bucket is 736 × 1280 because both spatial dimensions must be
divisible by 32. Frame counts must satisfy (num_frames - 1) % 8 == 0.
Intended use
This model is intended for research, evaluation, and creative generation of short videos from text, with optional first-frame image conditioning. It can also serve as a starting point for domain-specific fine-tuning under the license terms.
The model is not intended to produce factual evidence, identify people, make high-impact automated decisions, or generate content that violates privacy, copyright, applicable law, or platform policies.
Limitations and bias
- Generated motion, anatomy, text rendering, object permanence, and physical interactions may be inconsistent, especially for crowded or highly dynamic scenes.
- Prompt following can degrade for long, ambiguous, or compositionally complex instructions.
- Image-conditioned generation can drift from fine details in the source image.
- Outputs can reflect social and cultural biases present in training data and in the separately loaded text encoder.
- The model does not independently verify whether generated content is factual, safe, or free of third-party rights.
Users should review outputs before publication, disclose synthetic media where appropriate, and add safeguards suited to their application.
Resources
- SANA repository
- SANA-Video 2.0 release PR
- SANA-Video 2.0 documentation
- Model zoo
- Verified 5B 720p release video
Citation
If you use SANA-Video, please cite the SANA-Video work linked from the project page. SANA-Video 2.0-specific citation information will be added when it becomes available.