WAN 2.1 T2V 480p delivers ultra-fast text-to-video generation with custom LoRA support for unlimited 480p AI videos. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Bereit
$0.125pro Durchlauf·~80 / $10
The video shows a [z00m_ca11] with four participants. In the top left box, a medieval knight in full armor adjusts his helmet. To his right, a pirate with a parrot on his shoulder. In the bottom left, a scientist in a lab coat scribbles on a whiteboard. In the bottom right, an alien in a suit waves awkwardly
Oil painting style, VanGogh, VanGogh style. 一个导弹飞向月球,撞击然后爆炸解体
Oil painting style,VanGogh,VanGogh style.A massive, towering robot with metallic armor plates and glowing mechanical components stands in the middle of a vast golden wheat field that stretches to the horizon. The robot, approximately 50 feet tall with industrial design elements and visible hydraulic joints, faces off against an unexpectedly animated SpongeBob SquarePants. SpongeBob, with his characteristic yellow spongy body, big blue eyes, and buck teeth, appears comically small compared to the robot but displays surprising agility as he bounces through the wheat. The contrast between the hard-edged mechanical robot and the cartoonish SpongeBob creates a surreal visual juxtaposition. The two engage in an epic battle, with the robot firing energy beams and swinging massive mechanical arms that flatten sections of wheat, creating circular patterns in the field. SpongeBob counters with his stretchy body capabilities, dodging attacks and occasionally landing on the robot's shoulders or head. Wheat stalks sway dramatically from the impact of their movements, sending golden particles floating into the air that catch the sunlight. The sky above features dramatic clouds that cast dynamic shadows across the battlefield as this unlikely confrontation unfolds. Impasto oil painting in the style of Van Gogh's, impressionistic painting,oil painting, loose brush strokes, canvas texture, impasto technique,Van Gogh style
Wan 2.1 T2V 480p LoRA Ultra Fast is a low-latency text-to-video model designed for rapid iteration. It generates short 480p clips from a single prompt, and supports adding LoRAs to steer style, characters, or motion patterns while keeping throughput high.
| Output | Duration | Price per run | Effective price per second |
|---|---|---|---|
| 480p T2V (LoRA) | 5s | $0.125 | $0.025/s |
| 480p T2V (LoRA) | 10s | $0.188 | $0.0188/s |
prompt (required): describe subject, action, scene, camera, and style
negative_prompt (optional): reduce blur, jitter, distortions, low-quality artifacts
loras (optional): up to 3 LoRAs, each with:
path: owner/model-name or a direct.safetensors URL
scale: LoRA strength (commonly around 0.6–1.0 to start)
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/t2v-480p-lora-ultra-fast with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read URLs from data.outputs. Examples for Wan 2.1 T2v 480p Lora Ultra Fast below.
# Submit the prediction
curl --fail-with-body --connect-timeout 10 --max-time 60 \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/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",
"size": "832*480",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 3,
"seed": -1
}'
# Wait at least 2 seconds, then poll. Safe GET requests may be retried.
curl --fail-with-body --connect-timeout 10 --max-time 30 \
--retry 4 --retry-all-errors --retry-delay 1 \
-X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# Start at 2 seconds and increase the interval for long-running tasks.
# Stop on completed, failed, cancelled, or timeout.// npm install wavespeed
const { Client } = require('wavespeed');
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
const client = new Client(apiKey);
try {
const result = await client.run("wavespeed-ai/wan-2.1/t2v-480p-lora-ultra-fast", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "832*480",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 3,
"seed": -1
}, {
timeout: 3600,
pollInterval: 2.0,
});
console.log(result.outputs);
} catch (error) {
console.error('Generation failed:', error);
process.exitCode = 1;
}# pip install wavespeed
import os
from wavespeed import Client
client = Client(api_key=os.environ["WAVESPEED_API_KEY"])
try:
output = client.run(
"wavespeed-ai/wan-2.1/t2v-480p-lora-ultra-fast",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "832*480",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 3,
"seed": -1
},
timeout=3600.0,
poll_interval=2.0,
)
print(output["outputs"])
except Exception as error:
raise SystemExit(f"Generation failed: {error}") from errorWan 2.1 T2v 480p Lora Ultra Fast is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. WAN 2.1 T2V 480p delivers ultra-fast text-to-video generation with custom LoRA support for unlimited 480p AI videos. 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 result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/wan-2.1-t2v-480p-lora-ultra-fast.
Wan 2.1 T2v 480p Lora Ultra Fast starts at $0.13 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-480p-lora-ultra-fast.
Average end-to-end generation time on WaveSpeedAI is around 40 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.