SANA-Video 2.0¶

SANA-Video 2.0 provides efficient text-to-video and text-image-to-video models built with hybrid linear/softmax attention and Attention Residuals. This release includes the model architecture, training and inference code, 480p and 720p reference configs for the 5B variant, and a post-trained 5B checkpoint for 720p, 8-second generation. The 14B config and checkpoint are not included yet.

Release status: Explore the project page, try the 4-step 5B 720p preview, or download the 50-step and 4-step preview checkpoints. The 14B checkpoint is coming soon.

Architecture¶

Model	Layers	Hidden size	Linear attention	Softmax anchors	Parameters
`SanaVideo2_5B`	32	2,560	20 heads × 128	8 layers, 10 heads × 256	4,466,980,960
`SanaVideo2_14B`	40	4,096	32 heads × 128	10 layers, 16 heads × 256	14,246,716,224

Both variants use:

75% gated bidirectional linear-attention layers and 25% dense softmax anchor layers;
SwiGLU feed-forward networks with a 4× expansion ratio;
Wan 3D rotary position embeddings;
shared, timestep-independent Attention Residual aggregation in blocks of eight layers;
(1, 1, 1) latent patches; and
the LTX 2.3 VAE contract: 128 latent channels with (8, 32, 32) temporal/spatial strides.

Model zoo¶

Model	Resolution	Checkpoint	Precision
SANA-Video 2.0 5B	720p, 193 frames at 24 FPS	SANA-Video_2.0_5B_720p	BF16 inference
SANA-Video 2.0 5B 4-step preview	720p, 81 frames at 16 FPS or 193 frames at 24 FPS	SANA-Video_2.0_5B_720p_4step	BF16 inference
SANA-Video 2.0 14B	480p	Coming soon	BF16

The released 5B checkpoint was jointly post-trained for text-to-video (T2V) and text-image-to-video (TI2V) generation with ReFL. It contains only the merged model state_dict; optimizer, scheduler, and standalone LoRA state are not included. The checkpoint preserves the source EMA tensor values and is cast to BF16 by the inference entry point.

The 4-step research preview is a full-model DMD EMA export initialized from the merged ReFL step-500 model. It is T2V-only and is not a LoRA adapter. The release artifact contains model tensors only, without optimizer or scheduler state.

Code layout¶

Model: diffusion/model/nets/sana_video2.py
Transformer blocks: diffusion/model/nets/sana_video2_blocks.py
Four-step sampler: diffusion/scheduler/fastvideo_dmd_sampler.py
Training: train_video_scripts/train_video_ivjoint_chunk.py
Inference: inference_video_scripts/inference_sana_video.py
Configs: configs/sana_video2/

SANA-Video 2.0 reuses the repository's existing video training and inference entry points. No separate version-specific runner is required.

Setup¶

Install the repository environment:

bash environment_setup.sh sana
conda activate sana

Place the Diffusers-format LTX 2.3 VAE under:

output/pretrained_models/LTX-2.3-Diffusers/

Alternatively, update vae.vae_pretrained in the selected YAML. The example configs use data/video_toy_data; replace data.data_dir with a SanaZipDataset-compatible dataset for training.

Inference¶

The prompt file contains one prompt per line. The released 720p model generates 193 frames at 24 FPS (about 8 seconds) in a 736×1280 bucket. It was evaluated with classifier-free guidance 8, flow shift 12, and 50 sampling steps.

Verified 5B 720p release example¶

The following 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.

Try the SANA-Video 2.0 5B 720p 4-step preview online, or reproduce the original 50-step sample below with its exact command.

Open or download the generated MP4.

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.

Text-to-video¶

This is the exact command used to generate the verified example above:

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

Four-step text-to-video preview¶

The DMD preview uses four fixed stochastic stages, CFG 1, BF16 latent noise, and the sana_shift6_dpm sigma profile. It supports a 5-second profile (81 frames at 16 FPS) and an 8-second profile (193 frames at 24 FPS), and does not support first-frame conditioning. The online preview defaults to the 5-second profile and RL LoRA scale 0.7. The flow_shift argument is retained for CLI consistency but is not applied by this sampler. Model construction stays at the source tower's 480 setting; custom_height_width controls the actual 736×1280 latent and MP4 dimensions.

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_4step/checkpoints/SANA_Video_2.0_5B_720p_4step.pth \
  --txt_file=asset/samples/sana_video2_5b_720p_demo.txt \
  --task=t2v \
  --model.image_size=480 \
  --custom_height_width='[736,1280]' \
  --sampling_algo=fastvideo_dmd_4step \
  --generator_sigma_profile=sana_shift6_dpm \
  --cfg_scale=1.0 \
  --flow_shift=1.0 \
  --motion_score=20 \
  --negative_prompt=None \
  --num_frames=81 \
  --step=4 \
  --fps=16 \
  --seed=4 \
  --work_dir output/sana_video2_t2v_720p_4step_preview

The local command above runs the full checkpoint at its native RL scale 1.0. The verified online sample below uses the same prompt, schedule, duration, motion score, and seed, with the Space default RL LoRA scale 0.7 (1280 × 736, 81 frames, 16 FPS, 5.06 seconds). Reproduce those exact online settings with:

from pathlib import Path

from gradio_client import Client

prompt = Path("asset/samples/sana_video2_5b_720p_demo.txt").read_text().strip()
video_path, seed = Client("Efficient-Large-Model/sana-video2-5b-720p-demo").predict(
    prompt=prompt,
    video_duration="5 seconds",
    rl_lora_scale=0.7,
    motion_score=20,
    seed=4,
    randomize_seed=False,
    api_name="/generate",
)

Open or download the verified four-step video

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

Height and width must be divisible by 32, and frame counts must satisfy (num_frames - 1) % 8 == 0.

Training¶

The reference configs run video-only FSDP training with gradient checkpointing, online Gemma 2 caption encoding, causal LTX 2.3 VAE encoding, and flow matching.

torchrun --nproc_per_node=8 --master_port=29500 \
  train_video_scripts/train_video_ivjoint_chunk.py \
  --config_path configs/sana_video2/SanaVideo2_5B_480p.yaml

The released 720p config can also be used as the starting point for 5B fine-tuning:

torchrun --nproc_per_node=8 --master_port=29500 \
  train_video_scripts/train_video_ivjoint_chunk.py \
  --config_path configs/sana_video2/SanaVideo2_5B_720p.yaml

To resume, pass --resume_from=<checkpoint>. Set model.load_from when initializing from a compatible model checkpoint; leave it as null to train from scratch.