WAN 2.1 Text-to-Video 720P delivers unlimited ultra-fast videos from text prompts and supports custom LoRAs for personalized styles. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle

$0.225per run·~44 / $10
A small vietnamese village is on [r3al_f1re] with many wood houses burning, smoke filling the air, with flames consuming the dry grass and smoke filling the sky above
[actsoonr] movie clip close up of a singing actsoonr, tall thin green-eyed with asymmetrical brown haircut, riding a crimson horse through Leningrad at dusk, next to the river Neva embankment, whilst singing with smug passionate intensity, gesturing dramatically and theatrically, leaning close to the camera, passing next to the fast-flowing Neva river where a sleeping red-haired woman in greenish Soviet army uniform floats over currents nearby, whilst camera zooms in slowly onto actsoonr face looking over, extremely sharp extreme close-up, crisp 8k dramatic action shot, detailed skin. raw details, fast extreme clip, professional footage close up of actsoonr. Extremely detailed visceral cinematography, crisp detailed close up cinematic scene of actsoonr. Crisp 8k UHD DSLR professional color-corrected detailed footage.
Wan 2.1 Text-to-Video 720p LoRA Ultra Fast is a lightning-fast text-to-video generation model with full LoRA support. Generate HD 720p videos from text descriptions in seconds, with custom styles and effects — perfect for rapid iteration and high-volume content creation.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the video you want to generate. |
| negative_prompt | No | Elements to avoid in the output. |
| loras | No | LoRA models to apply (path and scale). |
| size | No | Output resolution: 1280×720 (default: 1280×720). |
| num_inference_steps | No | Quality/speed trade-off (default: 30). |
| duration | No | Video length: 5 or 10 seconds (default: 5). |
| guidance_scale | No | Prompt adherence strength (default: 5). |
| flow_shift | No | Motion flow control (default: 5). |
| seed | No | Set for reproducibility; -1 for random. |
LoRA (Low-Rank Adaptation) lets you apply custom styles without retraining the full model.
| Duration | Price |
|---|---|
| 5 seconds | $0.225 |
| 10 seconds | $0.3375 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/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 2.1 T2v 720p Lora Ultra Fast below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/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",
"loras": [
{
"path": "Remade-AI/Fire",
"scale": 1
}
],
"size": "1280*720",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 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-2.1/t2v-720p-lora-ultra-fast", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"negative_prompt": "blurry, low quality, distorted",
"loras": [
{
"path": "Remade-AI/Fire",
"scale": 1
}
],
"size": "1280*720",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 5,
"seed": -1
});
console.log(result.outputs[0]); // → URL of the generated output# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/wan-2.1/t2v-720p-lora-ultra-fast",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"negative_prompt": "blurry, low quality, distorted",
"loras": [
{
"path": "Remade-AI/Fire",
"scale": 1
}
],
"size": "1280*720",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 5,
"seed": -1
}
)
print(output["outputs"][0]) # → URL of the generated outputWan 2.1 T2v 720p Lora Ultra Fast is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. WAN 2.1 Text-to-Video 720P delivers unlimited ultra-fast videos from text prompts and supports 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-2.1-t2v-720p-lora-ultra-fast.
Wan 2.1 T2v 720p Lora Ultra Fast starts at $0.23 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`, `guidance_scale`, `num_inference_steps`. 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-2.1-t2v-720p-lora-ultra-fast.
Average end-to-end generation time on WaveSpeedAI is around 66 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.