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.
Idle
$0.125per run·~80 / $10
ExamplesView all
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
Related Models
README
Wan 2.1 T2V 480p LoRA Ultra Fast — wavespeed-ai/wan-2.1/t2v-480p-lora-ultra-fast
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.
Key capabilities
- Text-to-video generation at 480p
- Ultra-fast inference for quick previews and batch exploration
- LoRA support for style/character control (up to 3 LoRAs per run)
- Prompt-driven motion, staging, and shot direction
- Works well for storyboard drafts, social content, and concept tests
Use cases
- Fast storyboard and pre-vis from a director-style prompt
- Stylized “template clips” using LoRAs (brand look, character identity, anime/toy/film looks)
- Social content ideation: generate 10–20 variations quickly, pick the best, upscale later
- LoRA-driven series: consistent visual language across multiple clips for campaigns
Pricing
| 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 |
Inputs
-
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)
Parameters
- duration: clip length (commonly 5s or 10s)
- size: output size preset (e.g., 832×480)
- num_inference_steps: more steps can improve stability/detail at the cost of speed
- guidance_scale: higher values follow the prompt more strongly (can reduce natural motion if too high)
- flow_shift: motion behavior tuning (useful for more/less dynamic motion)
- seed: set for reproducible results (-1 for random)
Prompting tips (T2V)
- Write like a shot list: subject + action + environment + camera + style
- Keep motion explicit: “slow pan left”, “subtle head turn”, “hands gesture while talking”
- For multi-panel or UI-like scenes (e.g., a video call), specify layout and per-panel actions clearly
- If using LoRAs, keep the base prompt simpler and let the LoRA carry most of the style signal
Wan 2.1 T2v 480p Lora Ultra Fast API — Quick 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; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Wan 2.1 T2v 480p Lora Ultra Fast below.
HTTP example
# Submit the prediction
curl -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",
"negative_prompt": "blurry, low quality, distorted",
"loras": [
{
"path": "Remade-AI/Zoom-Call",
"scale": 1
}
],
"size": "832*480",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 3,
"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].Node.js example
// 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-480p-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/Zoom-Call",
"scale": 1
}
],
"size": "832*480",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 3,
"seed": -1
});
console.log(result.outputs[0]); // → URL of the generated outputPython example
# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/wan-2.1/t2v-480p-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/Zoom-Call",
"scale": 1
}
],
"size": "832*480",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 3,
"seed": -1
}
)
print(output["outputs"][0]) # → URL of the generated outputWan 2.1 T2v 480p Lora Ultra Fast API — Frequently asked questions
What is the Wan 2.1 T2v 480p Lora Ultra Fast API?
Wan 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.
How do I call the Wan 2.1 T2v 480p Lora Ultra Fast API?
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-480p-lora-ultra-fast.
How much does Wan 2.1 T2v 480p Lora Ultra Fast cost per run?
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.
What inputs does Wan 2.1 T2v 480p Lora Ultra Fast accept?
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.
How long does Wan 2.1 T2v 480p Lora Ultra Fast take to generate?
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.
Can I use Wan 2.1 T2v 480p Lora Ultra Fast outputs commercially?
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.