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SANA-Video 2B

Native text-to-video and image-to-video generation at 480p and 720p, plus a validated Diffusers-adapter compatibility path

Summary

  • Vendor: NVIDIA
  • Model: Efficient-Large-Model/SANA-Video_2B_480p_diffusers, Efficient-Large-Model/SANA-Video_2B_720p_diffusers
  • Task: Text-to-video and image-to-video
  • Mode: Offline inference and OpenAI-compatible online serving
  • Maintainer: Community

When to use this recipe

Use the native SanaVideoPipeline for T2V and SanaImageToVideoPipeline for I2V. Both native pipelines support the 480p and 720p checkpoints. Use --diffusion-load-format diffusers when you need the black-box Diffusers compatibility baseline; adapter T2V and I2V are validated at both resolutions.

The native pipeline loads the 480p checkpoint through DistributedAutoencoderKLWan and the 720p checkpoint through DistributedAutoencoderKLLTX2Video. These are vLLM-Omni distributed wrappers around the corresponding Diffusers autoencoders, not independent VAE implementations. The denoising loop also intentionally loads Diffusers' DPMSolverMultistepScheduler from the checkpoint to preserve its scheduler configuration and numerical behavior.

References

Hardware Support

GPU

1x RTX 5090 32GB

Environment

  • OS: Ubuntu 22.04.5 LTS
  • Python: 3.12.3
  • Driver / runtime: NVIDIA driver 580.95.05; PyTorch 2.11.0+cu130
  • Diffusers: 0.38.0
  • vLLM version: 0.26.0
  • vLLM-Omni version or commit: PR #5508, commit 22037901

Command

Native text-to-video inference
python examples/offline_inference/text_to_video/text_to_video.py \
  --model Efficient-Large-Model/SANA-Video_2B_720p_diffusers \
  --model-class-name SanaVideoPipeline \
  --prompt "A cat walking on the grass, facing the camera." \
  --negative-prompt "blurry, low quality, temporal artifacts" \
  --height 704 --width 1280 --num-frames 81 \
  --num-inference-steps 50 --guidance-scale 6 \
  --extra-body '{"motion_score": 30}' \
  --fps 16 --seed 42 --output sana_video_720p.mp4

For 480p, select SANA-Video_2B_480p_diffusers and use --height 480 --width 832.

Native image-to-video inference

SANA checkpoints declare SanaVideoPipeline in model_index.json, so I2V must be selected explicitly with --model-class-name SanaImageToVideoPipeline.

python examples/offline_inference/image_to_video/image_to_video.py \
  --model Efficient-Large-Model/SANA-Video_2B_480p_diffusers \
  --model-class-name SanaImageToVideoPipeline \
  --image input.jpg \
  --prompt "A cat turns toward the camera with smooth, natural motion." \
  --negative-prompt "blurry, low quality, temporal artifacts" \
  --height 480 --width 832 --num-frames 81 \
  --num-inference-steps 50 --guidance-scale 6 \
  --extra-body '{"motion_score": 30}' \
  --fps 16 --seed 42 --output sana_video_i2v_480p.mp4

The same pipeline class supports the 720p checkpoint through vLLM-Omni's distributed LTX-2 VAE wrapper; use --height 704 --width 1280.

For online I2V serving:

MODEL=Efficient-Large-Model/SANA-Video_2B_480p_diffusers \
  bash examples/online_serving/image_to_video/run_server_sana_video.sh

INPUT_IMAGE=input.jpg OUTPUT_PATH=sana_video_i2v.mp4 \
  bash examples/online_serving/image_to_video/run_curl_sana_video.sh
Native online serving
MODEL=Efficient-Large-Model/SANA-Video_2B_480p_diffusers \
  bash examples/online_serving/text_to_video/run_server_sana_video.sh

bash examples/online_serving/text_to_video/run_curl_sana_video.sh
Parallel native serving

The native pipelines support tensor parallelism (up to 2 GPUs), sequence parallelism (2 or 4 GPUs) and CFG parallelism. Tensor parallelism on 2 GPUs:

vllm serve Efficient-Large-Model/SANA-Video_2B_480p_diffusers \
  --omni \
  --model-class-name SanaVideoPipeline \
  --tensor-parallel-size 2 \
  --dtype bfloat16 \
  --port 8091

Sequence parallelism on 2 GPUs (replace --tensor-parallel-size 2 with):

  --usp 2

The --usp flag carries the sequence parallel degree. On SANA-Video the implementation is not Ulysses all-to-all: video frames are sharded across ranks and the linear-attention state is combined with one small all-reduce per block, which is far less communication than tensor parallelism needs. Ring and AllGather-KV sequence parallel do not apply to SANA's linear attention and are rejected.

For CFG parallelism on 2 GPUs, use --cfg-parallel-size 2 instead; it splits the guided and unguided branches across the two GPUs and only helps when guidance_scale is above 1. CFG parallelism combines with either tensor parallelism or sequence parallelism on 4 GPUs. Tensor and sequence parallelism also combine; on the measured 4-GPU NVLink box tp2 x sp2 reached 2.55x, below the pure sp4 (3.24x) and sp2 x cfg2 (3.56x) configurations, so prefer those when four GPUs serve one request.

Whether these lower latency depends on the interconnect. Each transformer block adds an all-reduce under tensor parallelism, so it speeds up generation only on fast GPU links such as NVLink and can be slower than a single GPU on PCIe-only systems. Sequence parallelism communicates far less per block, but was only measured on NVLink. Measure on your hardware before enabling either.

To run the black-box compatibility backend for T2V, replace the server script with run_server_sana_video_diffusers.sh. The same /v1/videos request works; num_frames is adapted to Diffusers' frames argument. The script selects TORCH_SDPA because SANA-Video uses an attention mask that the AITER-backed Diffusers attention path does not accept.

Diffusers-adapter image-to-video serving

The validated I2V adapter commands are:

# 480p
MODEL=Efficient-Large-Model/SANA-Video_2B_480p_diffusers \
  bash examples/online_serving/image_to_video/run_server_sana_video_diffusers.sh

INPUT_IMAGE=input.jpg OUTPUT_PATH=sana_video_i2v_adapter.mp4 \
  bash examples/online_serving/image_to_video/run_curl_sana_video.sh

# 720p
MODEL=Efficient-Large-Model/SANA-Video_2B_720p_diffusers \
  bash examples/online_serving/image_to_video/run_server_sana_video_diffusers.sh

INPUT_IMAGE=input.jpg WIDTH=1280 HEIGHT=704 \
  OUTPUT_PATH=sana_video_i2v_adapter_720p.mp4 \
  bash examples/online_serving/image_to_video/run_curl_sana_video.sh

Verification

Check the encoded 720p output metadata after running a generation command:

ffprobe -v error -select_streams v:0 \
  -show_entries stream=width,height,r_frame_rate,nb_frames \
  -of default=noprint_wrappers=1 sana_video_720p.mp4

The standard 720p request above should report:

width=1280
height=704
r_frame_rate=16/1
nb_frames=81

For 480p, expect width=832 and height=480.

The automated serving matrix covers both checkpoint variants:

Backend 480p T2V 720p T2V 480p I2V 720p I2V
Native vLLM-Omni Validated Validated Validated Validated
Diffusers adapter Validated Validated Validated Validated

Use the native SanaVideoPipeline and SanaImageToVideoPipeline for the primary SANA execution paths. The Diffusers adapter is retained as a validated compatibility/reference backend.

Native Cache-DiT and CPU offload

The native T2V and I2V pipelines support Cache-DiT, model-level CPU offload, and layerwise CPU offload through the common diffusion flags. Layerwise mode keeps non-block transformer modules, the text encoder, and the VAE on the runtime device while prefetching DiT blocks in order.

Add one of the following flag sets to either native offline command above:

# Cache-DiT
--cache-backend cache_dit

# Model-level CPU offload
--enable-cpu-offload

# Layerwise CPU offload
--enable-layerwise-offload

# Cache-DiT plus model-level offload
--cache-backend cache_dit --enable-cpu-offload

# Cache-DiT plus layerwise offload
--cache-backend cache_dit --enable-layerwise-offload

# Distributed layerwise offload (weights sharded across the DP group)
--enable-distributed-layerwise-offload

If both CPU offload flags are supplied, the common offloader keeps its existing layerwise precedence. Cache-DiT refreshes each request from the explicit num_inference_steps; when that field is omitted, both native SANA pipelines default to 50 inference steps.

Cache-DiT and CPU offload require TP1, CFG1, and SP1. Combining them with tensor, CFG, or sequence parallelism raises before checkpoint components are loaded, as do other cache backends such as TeaCache. Cache-DiT cannot be combined with distributed layerwise offload: per-rank cache skips would desynchronize the weight AllGather.

Measured on one A800-SXM4-80GB (driver 580.126.09, CUDA 13.0, PyTorch 2.11.0+cu130, Diffusers 0.38.0) with DIFFUSION_ATTENTION_BACKEND=CUDNN_ATTN, 81 frames, 50 steps, seed 42, each configuration in its own process:

Configuration 480p T2V latency 480p T2V generation peak
Baseline 120.19 s 24.07 GiB
Cache-DiT 77.11 s (1.56x) 24.07 GiB
Model CPU offload 121.15 s 16.81 GiB (-7.26 GiB)
Layerwise offload 118.63 s 20.75 GiB (-3.32 GiB)

Cache-DiT gives 1.54x on 720p I2V (37.0 s to 24.1 s). Latency is the median of three measured runs after one warmup. Offload trades startup peak too: 14.28 GiB baseline against 6.14 GiB for model-level and 6.80 GiB for layerwise.

Cache-DiT is approximate: it skips block computations, so its output differs from an uncached run. Similarity against a reference run is not used as a gate here because this pipeline does not reproduce its own trajectory on this hardware -- two runs of identical code differ by SSIM 0.9712 / rel_l2 8.7e-2 at 50 steps. Cache-DiT output was checked frame by frame against the uncached run on 480p T2V and 720p I2V instead, and matches it in subject, composition, sharpness, and lighting.

The numbers above use the offline example's default cache configuration. Lowering residual_diff_threshold in cache_config caches fewer steps, trading speed for a trajectory closer to the uncached run; see the Cache-DiT guide for the tuning knobs.

Notes

  • Key flags: select I2V explicitly with --model-class-name SanaImageToVideoPipeline; SANA checkpoint model_index.json files declare the T2V class. Pass motion_score through --extra-body. The supplied Diffusers-adapter server scripts select TORCH_SDPA because the AITER-backed path does not accept SANA's attention mask.
  • Output profile: 81 frames at 16 FPS is the standard checkpoint profile and produces approximately five seconds of video. Minute-scale generation requires the separate LongSANA/LongLive block-autoregressive workflow.
  • Backend boundary: the native pipelines and Transformer are owned by vLLM-Omni. The 480p Wan VAE and 720p LTX-2 VAE run through vLLM-Omni distributed wrappers derived from the corresponding Diffusers VAE classes. The denoising loop intentionally retains the checkpoint-compatible Diffusers DPMSolverMultistepScheduler.
  • Known limitations:
    • Tensor parallelism (up to 2 GPUs), sequence parallelism (2 or 4 GPUs) and CFG parallelism are supported for the native pipeline. TeaCache and step execution are not validated.
    • Cache-DiT and CPU offload are limited to TP1/CFG1/SP1. Cache-DiT with distributed layerwise offload is not supported by the native pipeline.
    • Cache-DiT speedup and offload memory numbers above are single-GPU A800 measurements; other hardware will differ. Distributed layerwise offload is only exercised single-rank here.
    • The Diffusers backend is a compatibility path and does not provide native vLLM-Omni parallelism or continuous batching.
    • Native describes pipeline and Transformer ownership, not a zero-Diffusers dependency guarantee.

1x NVIDIA L20X

Environment

  • Driver: 570.133.20
  • PyTorch: 2.13.0+cu130
  • Diffusers: 0.38.0
  • vLLM: 0.27.0
  • Nsight Systems: 2026.1
  • vLLM-Omni commit: 54fcc6d

Same-workload T2V comparison

Both backends used Efficient-Large-Model/SANA-Video_2B_480p_diffusers, the same T2V prompt and seeds, 832×480 output, 81 frames, and four inference steps. One warmup request was excluded, followed by three measured requests.

Metric Native vLLM-Omni Diffusers adapter Native delta
E2E latency 7.1600 ± 0.0043 s 6.9761 ± 0.0517 s +2.636%
Throughput 0.1397 req/s 0.1433 req/s -2.568%
Peak reserved VRAM 23,936 MiB 19,060 MiB +25.582%
Initialization 11.669 s 20.236 s -42.336%

The peak-memory result is not a Transformer-only comparison. For the 480p checkpoint, the native pipeline deliberately loads and decodes with the Wan VAE in FP32. This matches the upstream SANA-Video reference and the recorded accuracy-golden setup for this integration. The adapter launch uses --dtype bfloat16 as the Diffusers pipeline-wide dtype, so its Wan VAE also runs in BF16.

Peak VRAM is the CUDA allocator's maximum reserved memory over the complete request. It includes VAE decode activations and allocator-reserved blocks, not only live Transformer tensors. The different VAE precision policies are a material code-level difference and a likely contributor to the higher native high-water mark; the available full-request measurements do not isolate how much of the delta they account for. The 25.582% result should therefore not be interpreted as an isolated native Transformer memory regression. Exact attribution requires both paths to use the same VAE dtype. Changing the native 480p VAE to BF16 would instead change the recorded golden/reference setup and requires a new accuracy study.

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