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

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 checkpoint
• config.yaml: matching SANA training and inference configuration
• LICENSE: 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.

▶ Watch or download the generated video

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.