Wan 2.2 t2v 480p Ultra-Fast generates unlimited AI videos from text prompts at 480p with ultra-fast inference. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.05per run·~20 / $1
Ultra-wide lens tracking shot through neon-lit alleyways, pouring rain bouncing off puddles, camera whipping to follow a black-suited operative sprinting at full speed, dual pistols drawn. Explosions erupt in the background, showering debris in slow motion. Lens flares streak across the frame, smoke and sparks filling the air. The hero vaults over a burning car, slides under a closing metal shutter in one fluid motion. Close-up as rainwater drips from their jawline, eyes locked in determination. The final beat: a massive explosion backlighting the silhouette as they reload in slow motion — hyper-realistic, 24fps, Dolby-cinema color grading, John Wick × The Matrix tone.
Two teenagers, one in a vibrant red tank top and the other in a worn-out gray hoodie, are engaged in a one-on-one basketball game on a sun-drenched, graffiti-covered court in an urban park. The camera, in a shaky, close-up, handheld style, tracks the ball as it bounces rhythmically, capturing the quick footwork, beads of sweat on their foreheads, and the intense eye contact between them. The sound of sneakers squeaking on the asphalt, the rhythmic thud of the ball, and muffled cheers from a small group of onlookers create a lively, energetic atmosphere. The scene feels like a candid, unfiltered moment of competition and fun.
A playful golden retriever, with a wagging tail and a slobbery toy in its mouth, eagerly tries to get its owner's attention. The owner, a young man sitting on a comfortable living room sofa, smiles and playfully tussles with the dog. The camera, in a casual, close-up shot, captures the genuine joy and affection between them, focusing on the dog's joyful eyes and the man's warm laughter. Sunlight streams in through a nearby window, creating a cozy and warm atmosphere. The only sounds are the dog's happy whines, the man's laughter, and the soft thump of the dog's tail against the couch.
A high-energy rock band is performing on a dimly lit, smoky stage. The lead guitarist, a young man with long, wild hair, is passionately playing a blistering solo. Flashing red and blue stage lights strobe across his face and the chrome of his guitar. The camera is positioned low, capturing the aggressive strumming and the intense facial expressions of the band members. In the background, the silhouette of a cheering, head-banging crowd is visible. The motion is chaotic and fast-paced, with a sense of raw, unbridled energy and passion. The video has a grainy, vintage concert film aesthetic.
A young woman walking through a bustling city street at sunset, soft golden light reflecting on the buildings, her hair gently blowing in the breeze, ambient city sounds mixing with distant conversations
A female special agent in tactical gear, performing a rapid CQC (Close Quarters Combat) takedown on an enemy guard in a dimly lit, narrow corridor. Fast-paced action, tracking shot, motion blur, cinematic lighting, hyper-detailed, 8K.
An adventurer sprinting through a collapsing ancient temple, dodging falling pillars and fireballs. Dolly zoom shot showing the overwhelming danger behind him, volumetric lighting through dust clouds, Unreal Engine render, epic fantasy style.
A vintage muscle car skidding around a sharp corner on a mountain road, chasing a futuristic motorcycle. Low-angle tracking shot from the side, dust and debris kicking up from the tires, motion blur, anamorphic lens flare, sunset lighting, cinematic, high-octane.
A helicopter flies dramatically past a glass skyscraper just as another helicopter crashes into it and explodes in a massive fireball. Filmed from a third helicopter to create a sense of aerial scale. Slow-motion debris, including glass and metal, rains down towards the street below. Michael Bay style.
A high-speed motorcycle chase through the narrow, winding alleys of an ancient European city. The shot alternates between a drone shot following from above and a low-angle action cam attached to the bike, showing the cobblestones rushing by. The rider weaves impossibly between obstacles.
A character performs parkour across rooftops at sunset. Extreme slow-motion as they leap across a wide gap between buildings. A sweeping, wide-angle panoramic shot follows the jump, showcasing the sprawling city skyline below. The golden hour light creates dramatic silhouettes and lens flares.
On the slick streets of a neon-drenched cyberpunk city, the protagonist executes a stylish power slide across the wet ground, dual-wielding pistols and firing at pursuing cyborgs. A low-angle tracking shot follows the action closely. Bullet-time effect, clearly showing the muzzle flashes and the trajectory of bullets through the air. Abundant sparks from ricocheting bullets.
Unleash the power of Wan 2.2, a next-generation multimodal generative model from WAN AI. Built with an innovative Mixture of Experts (MoE) architecture, it combines high-noise and low-noise expert models across denoising timesteps to deliver cinematic-quality video content.
Per-second billing with a 5-second minimum.
| Output time | Cost |
|---|---|
| 5s | $0.05 |
| 8s | $0.08 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/t2v-480p-ultra-fast with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Wan 2.2 T2v 480p Ultra Fast below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/t2v-480p-ultra-fast" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"negative_prompt": "blurry, low quality, distorted",
"size": "832*480",
"duration": 5,
"seed": -1
}'
# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# When status is "completed", read the output from data.outputs[0].// npm install wavespeed
const WaveSpeed = require('wavespeed');
const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env
const result = await client.run("wavespeed-ai/wan-2.2/t2v-480p-ultra-fast", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"negative_prompt": "blurry, low quality, distorted",
"size": "832*480",
"duration": 5,
"seed": -1
});
console.log(result.outputs[0]); // → URL of the generated output# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/wan-2.2/t2v-480p-ultra-fast",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"negative_prompt": "blurry, low quality, distorted",
"size": "832*480",
"duration": 5,
"seed": -1
}
)
print(output["outputs"][0]) # → URL of the generated outputWan 2.2 T2v 480p Ultra Fast is a WaveSpeedAI model for video generation, exposed as a REST API on WaveSpeedAI. Wan 2.2 t2v 480p Ultra-Fast generates unlimited AI videos from text prompts at 480p with ultra-fast inference. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/wan-2.2-t2v-480p-ultra-fast.
Wan 2.2 T2v 480p Ultra Fast starts at $0.050 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `duration`, `size`, `seed`, `negative_prompt`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/wan-2.2-t2v-480p-ultra-fast.
Average end-to-end generation time on WaveSpeedAI is around 34 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.