Ultra-fast Wan 2.2 text-to-video model producing 480p videos with custom LoRA support—generate unlimited AI videos with personalized styles. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.1per run·~10 / $1
A stunt motorcyclist launches off a ramp as two cars explode beneath him, flames and debris bursting into the air, bike chrome catching the orange glow, heat distortion visible, jacket fluttering wildly, cinematic motion blur realism
A young man rides his bicycle through a quiet city street at night, wind ruffling his shirt. Passing headlights create flares in the lens, and streetlights cast long shadows. Slow-motion cut as he pedals uphill, then a rear tracking shot from low angle.
Extreme close-up as a boxing glove lands on a focus pad. The camera captures skin ripple, sound impact in sync with vibration. Drops of sweat fly off, face partially seen in background, muscles contract sharply. High frame rate, cinematic grading, everything slows during the hit.
A track athlete crouches at the starting line, breath visible in the cold morning air. As the starting shot fires, he bursts forward, dirt and dust spraying behind him. The camera tracks low beside him, shoes pounding the track rhythmically, sun rising in golden glow.
A snowboarder speeds down a powder-covered slope, carving hard, snow exploding in arcs behind. The camera follows just above shoulder, capturing twists and jumps. GoPro-style quick spins, lens smudged with snow, sun flares through the trees.
Two cyberpunk bounty hunters exchanging gunfire in a narrow neon alley, rain pouring, puddles reflecting muzzle flashes
A lone swordsman standing in the middle of a burning bridge, sparks and ash swirling in the air, dramatic backlight
A lone special forces soldier sliding under gunfire in a war-torn city, muzzle flash illuminating dust in the air, cinematic lighting, ultra realistic, 4K
A battle-hardened special forces soldier dashes across a war-torn street under heavy gunfire, explosions lighting the ruined buildings behind him, dust and concrete shards flying with each step, sweat and grime covering his face, ultra cinematic lighting, photorealistic details
An archer stands on a snowy cliff edge in a raging blizzard, drawing his bow against the wind, frost clinging to his armor, snow swirling violently around, distant mountains fading into white mist, cinematic cold lighting and ultra sharp detail
Generate customized videos from text prompts with LoRA support using Wan 2.2 Ultra Fast. This budget-friendly model creates 480p videos at blazing speed while allowing you to apply custom LoRA adapters — perfect for rapid prototyping, style testing, and high-volume content creation.
Looking for higher resolution? Try Wan 2.2 I2V 720p LoRA Ultra Fast for HD output.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the scene, motion, and action you want. |
| negative_prompt | No | Elements to avoid in the generated video. |
| duration | No | Video length: 5 or 8 seconds. Default: 5. |
| loras | No | Standard LoRA adapters to apply (up to 3). |
| high_noise_loras | No | LoRAs applied during high-noise denoising stages (up to 3). |
| low_noise_loras | No | LoRAs applied during low-noise denoising stages (up to 3). |
| seed | No | Random seed for reproducibility. Use -1 for random. |
| Enable Safety Checker | No | Toggle content safety filtering. |
Per 5-second billing based on duration.
| Duration | Calculation | Cost |
|---|---|---|
| 5 seconds | 5 ÷ 5 × $0.10 | $0.10 |
| 8 seconds | 8 ÷ 5 × $0.10 | $0.16 |
| Videos | Duration | Total Cost |
|---|---|---|
| 10 | 5s | $1.00 |
| 10 | 8s | $1.60 |
| 100 | 5s | $10.00 |
| 100 | 8s | $16.00 |
This model provides three different LoRA slots that affect different stages of the generation process:
| LoRA Type | When Applied | Best For | Max Count |
|---|---|---|---|
| loras | Throughout generation | General style, character consistency | 3 |
| high_noise_loras | Early denoising (high noise) | Overall composition, major style elements | 3 |
| low_noise_loras | Late denoising (low noise) | Fine details, textures, finishing touches | 3 |
loras for consistent style throughout.For detailed guides on using and training custom LoRAs:
| Model | Resolution | Cost (5s) | Best For |
|---|---|---|---|
| T2V 480p LoRA Ultra Fast | 480p | $0.10 | Testing, prototyping, high-volume |
| I2V 720p LoRA Ultra Fast | 720p | $0.15 | Final delivery, quality content |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/t2v-480p-lora-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 Lora Ultra Fast below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/t2v-480p-lora-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-lora-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-lora-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 Lora Ultra Fast is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Ultra-fast Wan 2.2 text-to-video model producing 480p videos with custom LoRA support—generate unlimited AI videos with personalized styles. 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-lora-ultra-fast.
Wan 2.2 T2v 480p Lora Ultra Fast starts at $0.10 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`, `high_noise_loras`. 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-lora-ultra-fast.
Average end-to-end generation time on WaveSpeedAI is around 42 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.