Efficient-Large-Model / SANA-Video_2.0_5B_720p

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

Checkpoint SHA256:

7e557554540b4cbbc515166b43a7d307285ab250cedfb48627878227a722d25a

Inference

Support for this checkpoint is provided by the SANA-Video 2.0 release branch while NVlabs/Sana PR #439 is under review:

git clone https://github.com/NVlabs/Sana.git
cd Sana
git checkout release/sana-video-2.0
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

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/video_prompts_samples.txt \
  --cfg_scale 8 \
  --flow_shift 12 \
  --step 50 \
  --fps 24 \
  --motion_score 20 \
  --work_dir output/sana_video2_t2v_720p

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

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.