vllm-omni/recipes/Wan-AI/Wan2.2-TI2V-5B-A100.md

Wan2.2 TI2V 5B — A100 80GB

Single-GPU, native 720p text-to-video and image-to-video serving

Summary

• Vendor: Wan-AI
• Model: Wan-AI/Wan2.2-TI2V-5B-Diffusers
• Task: Unified text-to-video (T2V) and image-to-video (I2V) generation
• Mode: Online serving with the OpenAI-compatible Videos API
• Hardware: 1x NVIDIA A100-SXM4-80GB
• Maintainer: Community

When to use this recipe

Use this recipe to serve both T2V and I2V from one dense 5B checkpoint on one A100 80GB. It qualifies the model's native 720p profile without CPU offload or multi-GPU parallelism. The synchronous endpoint is used below because it returns the MP4 and request timing in one response; the asynchronous Videos API uses the same server and sampling parameters.

Supported model contract

Task	Required input	Qualified output	Endpoint
T2V	Text prompt; omit input_reference	1280x704, 121 frames, 24 fps	POST /v1/videos/sync
I2V	Text prompt plus one image uploaded as input_reference	1280x704, 121 frames, 24 fps	POST /v1/videos/sync

The upstream checkpoint supports both tasks at 720p and 24 fps. For native landscape 720p, use 1280x704; portrait generation uses 704x1280. Wan video lengths follow the temporal VAE contract (4k+1 frames). This recipe validates one output per request with 50 denoising steps and seed 42.

References

• Upstream model card: https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers
• Online T2V example: examples/online_serving/text_to_video
• Online I2V example: examples/online_serving/image_to_video
• Offline I2V example: examples/offline_inference/image_to_video
• Community recipe tracker: vllm-project/vllm-omni#2645

Hardware

• Accelerator model and per-device memory: NVIDIA A100-SXM4-80GB, 81,920 MiB
• Number of devices: 1
• Device interconnect: not applicable to this single-GPU profile
• Host memory: 1 TiB installed on the qualification host; CPU offload was not used
• Qualification scope: BF16, batch size 1, native FlashAttention, eager mode, VAE tiling, T2V and I2V at 1280x704 / 121 frames / 24 fps

Software environment

• OS: Ubuntu 24.04.3 LTS, Linux 6.8.0
• Python: 3.12.3
• NVIDIA driver / host toolkit: 550.127.05 / CUDA 12.8
• PyTorch: 2.13.0+cu129
• Transformers / Diffusers: 5.14.1 / 0.40.0
• vLLM: 0.28.0+cu129
• vLLM-Omni: commit ff6e906a25de1ca3122c0d0d5250fde5a0ddbc7b

The default vLLM 0.28.0 wheel targets CUDA 13.0. Driver 550 cannot run that variant, so this qualification used the official CUDA 12.9 wheel:

uv venv --python 3.12 --seed
source .venv/bin/activate
uv pip install \
  'https://github.com/vllm-project/vllm/releases/download/v0.28.0/vllm-0.28.0%2Bcu129-cp38-abi3-manylinux_2_28_x86_64.whl' \
  --torch-backend=auto
uv pip install -e .

Command

Start the server from the repository root:

vllm serve Wan-AI/Wan2.2-TI2V-5B-Diffusers \
  --omni \
  --port 8092 \
  --vae-use-tiling \
  --enforce-eager

In another terminal, download the public reference image:

wget -O cherry_blossom.jpg \
  https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg

Generate a text-only video:

curl --fail-with-body -D t2v-headers.txt \
  -X POST http://127.0.0.1:8092/v1/videos/sync \
  -F "prompt=Two anthropomorphic cats in comfortable boxing gear and bright gloves fight intensely on a spotlighted stage, cinematic lighting, smooth motion." \
  -F "negative_prompt=blurry, low quality, static, distorted, artifacts, watermark, text" \
  -F "width=1280" -F "height=704" \
  -F "num_frames=121" -F "fps=24" \
  -F "num_inference_steps=50" -F "guidance_scale=5.0" \
  -F "flow_shift=5.0" -F "seed=42" \
  --output wan22-ti2v-5b-t2v.mp4

Generate an image-conditioned video from the same server:

curl --fail-with-body -D i2v-headers.txt \
  -X POST http://127.0.0.1:8092/v1/videos/sync \
  -F "input_reference=@cherry_blossom.jpg" \
  -F "prompt=Cherry blossoms sway gently in the breeze while petals drift across the path, cinematic, smooth natural motion." \
  -F "negative_prompt=blurry, low quality, static, distorted, artifacts, watermark, text" \
  -F "width=1280" -F "height=704" \
  -F "num_frames=121" -F "fps=24" \
  -F "num_inference_steps=50" -F "guidance_scale=5.0" \
  -F "flow_shift=5.0" -F "seed=42" \
  --output wan22-ti2v-5b-i2v.mp4

Verification

Check both MP4 files with ffprobe:

for video in wan22-ti2v-5b-t2v.mp4 wan22-ti2v-5b-i2v.mp4; do
  ffprobe -v error -select_streams v:0 -count_frames \
    -show_entries stream=codec_name,width,height,r_frame_rate,nb_read_frames,duration \
    -of default=noprint_wrappers=1 "$video"
done

Each file reported:

codec_name=h264
width=1280
height=704
r_frame_rate=24/1
duration=5.041667
nb_read_frames=121

The synchronous response headers and an independent 200 ms nvidia-smi sampler produced these steady-state results after server warmup:

Task	HTTP	X-Inference-Time-S	Server peak	Observed device peak	MP4 size
T2V	200	288.267 s	26,546 MB	27,533 MiB	7,321,179 bytes
I2V	200	299.439 s	29,766 MB	30,333 MiB	15,667,146 bytes

Both outputs were decoded in full and visually inspected. The T2V output contained the prompted boxing cats. The I2V first frame preserved the uploaded cherry-blossom scene (5.81 mean absolute pixel error after resize and H.264 encoding), and later frames showed motion.

Notes

• Memory usage: model loading used 21.2239 GiB. The measured I2V device peak was 30,333 MiB, leaving more than 50 GiB free on this A100.
• --vae-use-tiling is retained in the known-good command even though this profile has ample headroom; it bounds decode memory at 720p.
• --enforce-eager removes graph-compilation variability from the measured profile. Remove it only after separately warming and measuring compiled mode.
• flow_shift=5.0 is the 720p setting. The shared 480p examples use 12.0.
• The generated MP4 contains video only; this checkpoint does not generate audio.
• The first request includes neither model-load time nor the server's startup warmup. Server initialization took approximately 26 seconds with a warm cache.

Supported features

Feature	Status for this profile	Shared guide
OpenAI-compatible Videos API	Qualified for synchronous T2V and I2V	Videos API
VAE tiling	Enabled and qualified on one A100	VAE parallelism and tiling
Cache-DiT / TaylorSeer	Available but not enabled in the measurements above	Cache-DiT
TeaCache	Available but not qualified in this recipe	TeaCache
CPU or layerwise offload	Not needed for the qualified 80GB profile	CPU offload
TP / SP / CFG parallelism	Not applicable to this single-GPU profile	Parallelism overview