Wan 2.2 i2v delivers ultra-fast Image-to-Video at 480p with support for custom LoRAs for tailored styles. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.1per run·~10 / $1
ExamplesView all
A teen girl sits in front of her phone camera, talking animatedly, adjusting lighting, and reacting to comments. Posters, fairy lights, and stuffed animals in the background. Natural movement, expressive face, realistic indoor vibe.
A father gently supports his young son on a small bike, running beside him along a suburban street. The child wobbles, then finds balance. Camera follows from low angle, capturing motion, joy, and growth. Natural daylight, lens flares, suburban realism.
An elderly man with white hair gestures animatedly as he tells a story to three young children sitting at his feet in a cozy living room. A fireplace crackles in the background. The children giggle and gasp. Close-ups on facial expressions, warm ambient glow.
A middle-aged woman jogs through early-morning city streets, earbuds in, sweat on her brow. Traffic lights blink in the distance. The sun begins to rise between tall buildings. Rhythmic pacing, lens flares, urban realism.
A young artist paints on a rooftop canvas as the sun sets behind city buildings. She mixes colors on a palette, brushes long strokes, and steps back to observe. The sky is orange-pink. Gentle wind moves her hair and clothes. Soft textures, solitary beauty.
A woman stands in a wide wheat field, eyes closed, her scarf fluttering violently in the wind. Camera orbits slowly, lens catching sun flare from behind. Grains sway rhythmically, birds rise in the distance, shadows move across the land with cloud drift. Soft ambient soundscapes enhance solitude.
A heavy industrial machine slowly powers up inside a dimly lit warehouse, red and green lights blink in sequence. Camera rotates around the central motor as it begins to spin, slight vibration in the frame. Steam vents release puffs periodically, metallic clanks echo off concrete walls.
Explosive action sequence, car exploding in downtown, shards of glass flying, fire and smoke, realistic physics, hero diving away from explosion, cinematic wide-angle, slow motion effect, ultra-realistic lighting, high dynamic range, photorealistic, intense atmosphere
Tense shootout scene, hero taking cover behind abandoned car, muzzle flashes illuminating night alley, rain-soaked environment, dramatic camera angle, realistic smoke and sparks, 8K ultra-detailed, photorealistic textures, dynamic composition, high intensity action
Epic helicopter chase over city, hero hanging from helicopter skid, motion blur, realistic clouds and wind effects, cinematic lighting, photorealistic textures, dynamic camera angle, intense action, high detail, dramatic composition
Related Models
README
Wan-2.2-i2v-480p-lora-ultra-fast
Wan 2.2 (Ultra-Fast 480p, with LoRA) is a high-speed multimodal video generation model. It delivers cinematic-quality results at ultra-fast inference speed, with support for up to 3 LoRAs per job for flexible style and character control.
Key Features
-
Cinematic-level Aesthetic Control: Professional camera language, multi-dimensional control over lighting, color, and composition.
-
Large-scale Complex Motion: Smoothly restores natural motion, supports multi-subject dynamics, and enhances controllability.
-
Precise Semantic Compliance: Excels at complex scene understanding and multi-object generation, ensuring faithful creative intent.
-
LoRA Integration: Import up to 3 LoRAs per job for both high-noise and low-noise experts, with adjustable blending scale.
Limits and Performance
-
Resolution: 480p
-
Duration options: 5s or 8s
-
Input types:
-
Prompt
-
Image (First Frame)
-
Last Image (Last Frame)
-
LoRAs: up to 3 high-noise LoRAs + 3 low-noise LoRAs or just 3 LoRAs
-
Seed: reproducibility control
Pricing
| Duration | Cost |
|---|---|
| 5 seconds | $0.10 |
| 8 seconds | $0.16 |
How to Use
- Upload an initial image.
- Write a prompt describing the video scene.
- (Optional) Add a last_image for smooth transitions.
- Select duration (5s or 8s).
- Add LoRAs (up to 3 for high-noise experts, 3 for low-noise experts).
- (Optional) Set a seed for reproducibility.
- Run the job and preview/download your video.
Pro Tips
- Use image + last_image for storyboarding transitions.
- Apply high-noise LoRAs for global style changes, and low-noise LoRAs for subtle refinements.
- Keep LoRA scale values balanced (0.5–1.0 recommended) for natural blending.
- Choose 5s for quick iterations and 8s for polished results.
Note
- If you did not upload the image locally, please ensure that the image URL is accessible! A successfully accessible image will display a preview in the interface.
Reference
Wan 2.2 I2v 480p Lora Ultra Fast API — Quick start
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/i2v-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 I2v 480p Lora Ultra Fast below.
HTTP example
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/i2v-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",
"image": "https://example.com/your-input.jpg",
"negative_prompt": "blurry, low quality, distorted",
"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].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-2.2/i2v-480p-lora-ultra-fast", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"negative_prompt": "blurry, low quality, distorted",
"duration": 5,
"seed": -1
});
console.log(result.outputs[0]); // → URL of the generated outputPython example
# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/wan-2.2/i2v-480p-lora-ultra-fast",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"negative_prompt": "blurry, low quality, distorted",
"duration": 5,
"seed": -1
}
)
print(output["outputs"][0]) # → URL of the generated outputWan 2.2 I2v 480p Lora Ultra Fast API — Frequently asked questions
What is the Wan 2.2 I2v 480p Lora Ultra Fast API?
Wan 2.2 I2v 480p Lora Ultra Fast is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Wan 2.2 i2v delivers ultra-fast Image-to-Video at 480p with support for custom LoRAs for tailored 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.
How do I call the Wan 2.2 I2v 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-2.2-i2v-480p-lora-ultra-fast.
How much does Wan 2.2 I2v 480p Lora Ultra Fast cost per run?
Wan 2.2 I2v 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.
What inputs does Wan 2.2 I2v 480p Lora Ultra Fast accept?
Key inputs: `prompt`, `image`, `duration`, `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-i2v-480p-lora-ultra-fast.
How long does Wan 2.2 I2v 480p Lora Ultra Fast take to generate?
Average end-to-end generation time on WaveSpeedAI is around 54 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 2.2 I2v 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.