Ultra-fast Wan 2.2 Text-to-Video generates unlimited 720p AI videos with custom LoRAs for personalized styles. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle

$0.15per run·~66 / $10
A street dancer performs energetic moves in front of a crowd on a city square. His feet tap rhythmically, arms moving in sync with the beat. People cheer and record with their phones. Urban graffiti fills the background.
A cloaked figure runs through a neon-lit cyberpunk alley, steam rising from vents, digital signs flashing. The camera shakes subtly with each step, following closely from behind. Sparks burst as the figure brushes against a metal wall, drone whirring overhead, rain begins to fall, creating lens streaks.
A female assassin jumping from a rooftop with dual blades, city lights reflecting on her armor, motion blur, realistic skin texture, cinematic depth of field
Two boxers exchanging powerful punches under bright ring lights, sweat spraying from impact, hyper-real facial details, motion blur realism
A female spy diving sideways while firing dual pistols, empty shells suspended in the air, dust illuminated by gunfire
A desert warrior runs across shifting dunes under a blazing sun, scarf trailing behind in the wind, sand spraying from each step, distant dust cloud of pursuing riders visible, heat haze blurring the horizon, cinematic wide shot
A lone survivor walks through the smoking ruins of a bombed city, ashes falling like snow, distant fires still burning, weapon slung over his shoulder, expression cold and resolute, cinematic slow pan with ultra realistic debris and lighting
A special forces soldier sprinting through a battlefield, dirt and debris flying with each step, muzzle flash lighting his determined face, ultra photorealistic
A plainclothes detective sprints down a crowded subway platform, weaving through commuters, train doors closing in the background, jacket flying open with each step, sweat and determination on his face, cinematic natural lighting
A martial artist delivering a spinning kick in slow motion, sweat droplets visible, muscles tensed, motion blur realism
A cyclist struggles to push forward through torrential rain on a narrow bridge, raindrops blurring the vision, tires splashing through puddles, headlights reflecting off the wet pavement
A man blocks an incoming punch in a back-alley fight, breath visible in the cold night air, trash cans knocked over, neon shop lights casting sharp shadows, ultra detailed facial tension and muscle strain
Generate customized 720p HD videos from text at blazing speed with full LoRA support. This speed-optimized model delivers high-quality output with three LoRA slots for style control — perfect for rapid iteration, testing LoRA combinations, and high-volume production.
Need maximum quality? Try Wan 2.2 T2V 720p LoRA for standard processing with premium output.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the scene, action, and atmosphere you want. |
| negative_prompt | No | Elements to avoid in the generated video. |
| size | No | Output dimensions: 1280×720 (landscape) or 720×1280 (portrait). Default: 1280×720. |
| 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 × $0.15 | $0.15 |
| 8 seconds | 8 ÷ 5 × $0.15 | $0.24 |
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 |
| Size | Orientation | Best For |
|---|---|---|
| 1280×720 | Landscape | YouTube, presentations, desktop viewing |
| 720×1280 | Portrait | TikTok, Instagram Reels, Stories, mobile |
For detailed guides on using and training custom LoRAs:
| Model | Cost (5s) | Speed | Best For |
|---|---|---|---|
| T2V 720p LoRA Ultra Fast | $0.15 | Fast | Rapid iteration, testing, high-volume |
| T2V 720p LoRA | $0.20+ | Standard | Maximum quality with LoRA support |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/t2v-720p-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 720p Lora Ultra Fast below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/t2v-720p-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": "1280*720",
"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-720p-lora-ultra-fast", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"negative_prompt": "blurry, low quality, distorted",
"size": "1280*720",
"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-720p-lora-ultra-fast",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"negative_prompt": "blurry, low quality, distorted",
"size": "1280*720",
"duration": 5,
"seed": -1
}
)
print(output["outputs"][0]) # → URL of the generated outputWan 2.2 T2v 720p 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 generates unlimited 720p AI videos with custom LoRAs for 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-720p-lora-ultra-fast.
Wan 2.2 T2v 720p Lora Ultra Fast starts at $0.15 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-720p-lora-ultra-fast.
Average end-to-end generation time on WaveSpeedAI is around 94 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.