WAN 2.2 Image-to-Video (i2v) 720p converts images into 720p videos and supports custom LoRAs for style personalization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.35per run·~28 / $10
A handheld camera walks through a busy Middle Eastern bazaar. People bargain, children play, spices and cloths fill the frame. Rich saturated colors, dynamic shadows, shallow depth of field. Natural dialogue sounds faintly in the background. Organic human motion.
The video begins with a lego man. A hydraulic press positioned above slowly descends towards the man. Upon contact, the hydraulic press crushes it, deforming and flattening the man, causing the man to collapse inward until the man is no longer recognizable.
Golden sunlight flickers through the trees as a deer dashes through a dense forest. The camera chases low from behind, dodging between trunks. Dust and pollen glow in the backlight. Leaves swirl in the air. High frame-rate motion blur, natural tones, immersive movement.
Close-up of raindrops sliding down a train window. Outside, blurred countryside rushes past. Camera slowly rack-focuses from glass to the landscape. Soft lighting, cold color palette, moody and cinematic. Inside reflection of a traveler deep in thought.
A winged beast slowly rises from the misty lake at dawn. Camera dollies out and tilts up to reveal its full scale. Feathers shimmer with iridescent light, water drips from its wings. Epic orchestral mood, magical realism, high detail environment.
A diver floats weightlessly near a coral reef. Camera tracks from below, rising slowly. Sunlight filters through the water. Fish swarm past in slow motion. Peaceful, dreamlike, bioluminescent glow accents. Natural physics-based water motion, realistic bubbles and refraction.
A retro-style scientist moves around a 1960s sci-fi lab. Oscilloscopes blink, steam rises from glowing tubes. The camera pans left slowly with a long take. Desaturated retro colors, slight grain, vintage film look. Analog textures and blinking lights everywhere.
POV of a mountain biker speeding down a forest trail. Sudden drops, high-speed turns, dust kicking up. Natural camera shake, motion blur. Light flickers through the trees. Extreme sports realism, immersive audio. High frame rate, narrow FOV.
A log cabin emerges from a blizzard. The camera slowly pulls in through thick snow, flakes sticking to lens. Warm orange glow from the window contrasts with cold blue environment. Footprints in snow. Quiet, isolated, atmospheric.
Close-up of a barista making pour-over coffee in a sunlit kitchen. Steam rises from the kettle, coffee drips slowly into a glass carafe. Natural light hits the wood counter. Shallow depth of field, ASMR vibe, warm tones, real-time motion.
A crowded crosswalk in Tokyo during golden hour. People walk in every direction, warm light reflects off glass buildings. Long shadows stretch across the ground. The camera slowly pans left, revealing neon signs turning on. Urban atmosphere, documentary style.
Generate customized 720p HD videos from images with full LoRA support using Wan 2.2. This premium model delivers high-quality output with three LoRA slots for precise style control — perfect for professional content, cinematic scenes, and custom character animation.
Looking for faster processing? Try Wan 2.2 I2V 720p LoRA Ultra Fast for speed-optimized generation.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Source/starting image to animate (upload or public URL). |
| prompt | Yes | Text description of the motion and action you want. |
| negative_prompt | No | Elements to avoid in the generated video. |
| last_image | No | Optional ending frame for start-to-end interpolation (upload or URL). |
| 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 × $0.35 | $0.35 |
| 8 seconds | 8 ÷ 5 × $0.35 | $0.56 |
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 |
loras for consistent style throughout.| Model | Cost (5s) | Speed | Best For |
|---|---|---|---|
| I2V 720p LoRA | $0.35 | Standard | Maximum quality, final deliverables |
| I2V 720p LoRA Ultra Fast | $0.15 | Fast | Rapid iteration, testing, high-volume |
For detailed guides on using and training custom LoRAs:
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/i2v-720p-lora 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 720p Lora below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/i2v-720p-lora" \
-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].// 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-720p-lora", {
"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 output# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/wan-2.2/i2v-720p-lora",
{
"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 720p Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. WAN 2.2 Image-to-Video (i2v) 720p converts images into 720p videos and supports custom LoRAs for style personalization. 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-i2v-720p-lora.
Wan 2.2 I2v 720p Lora starts at $0.35 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`, `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-720p-lora.
Average end-to-end generation time on WaveSpeedAI is around 226 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.