WAN 2.1 i2v 720p is an ultra-fast Image-to-Video model that turns images into 720P videos and supports custom LoRAs for style control. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.225per run·~44 / $10
ExamplesView all
In the video, a warlord is presented. The warlord is held in a persons hands. The person then presses on the warlord, causing a sq41sh squish effect. The person keeps pressing down on the warlord, further showing the squish effect
美女模特坐在栏杆上，穿着漂亮的鞋子，晃动着双腿，开心的微笑，身边樱花飘落
The video begins with a chicken. A hydraulic press positioned above slowly descends towards the chicken. Upon contact, the hydraulic press c5us4 crushes it, deforming and flattening the chicken, causing the chicken to collapse inward until the chicken is no longer recognizable.
Young girl waving goodbye to the camera.
two beautiful happy women walking towards the camera in a natural way.
Create an animated effect for a children's music video. Show the cheerful green frog (Sapo Saltitão) sliding down a magical rainbow slide from the clouds into a pink jelly lake. Add floating balloons, sparkling confetti, and excited children clapping and laughing around him. The background should be a bright blue sky. Make the edges of the video shimmer with colorful rays to enhance the playful and circus-like atmosphere.
two beautiful happy women walking towards the camera in a natural way.
Luffy looks intently off-camera and the camera zooms out as Luffy p5lls g4un pulls a gun and starts shooting.
The video opens with a studio portrait of a man smiling in a white t-shirt. The pu11y puppy effect then begins, as puppies begin to gather and surround him. He is now holding a puppy in his arms.
The video begins with an image of a woman. A pr1nc355 princess transformation then happens as white sparkling light appears around the woman's shoulders and chest. The woman is now wearing a tiara on her head, a long, silver beaded gown, and silver gloves that reach up to her biceps. The woman is seated on a chair in front of a table with gifts and candles. The background is a room with white curtains. The woman is looking directly at the camera and holding her hand up. The camera pans to the right to display more gifts and candles. Thew man then moves their hand closer to the camera.
Related Models
README
Wan 2.1 I2V 720p LoRA Ultra Fast — wavespeed-ai/wan-2.1/i2v-720p-lora-ultra-fast
Wan 2.1 I2V 720p LoRA Ultra Fast is a high-speed image-to-video model that turns a single reference image into a short, coherent clip guided by your prompt. It supports up to 3 LoRAs per run, making it great for fast style exploration, character looks, and branded aesthetics while keeping generation time low.
Key capabilities
- SOTA Performance: Wan2.1 consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
- Multiple Tasks: Wan2.1 excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
- Visual Text Generation: Wan2.1 is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
- Powerful Video VAE: Wan-VAE delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
Use cases
- Bring key art to life: animate posters, portraits, product shots into short clips
- Style locking for content pipelines: apply one or more LoRAs for consistent aesthetics
- Character look development: combine identity/style LoRAs for repeatable outputs
- Social media loops: quick 5–10s clips from a single still image
- Marketing variations: same composition, different mood, lighting, wardrobe, brand style
Pricing
| Resolution | Duration | Price per run | Effective price per second |
|---|---|---|---|
| 720p | 5s | $0.225 | $0.045/s |
| 720p | 10s | $0.338 | $0.034/s |
Inputs
- image (required): reference image that anchors subject and composition
- prompt (required): what happens in the clip (action + camera + mood)
- loras (optional): up to 3 LoRA items (each with a path and a scale)
Parameters
- duration: clip length (commonly 5s or 10s)
- num_inference_steps: more steps can improve motion coherence and detail
- guidance_scale: higher values follow the prompt more strongly
- strength: how strongly the model follows the input image vs. deviating into new variations
- negative_prompt: describe what to avoid (blur, artifacts, jitter, low quality)
- flow_shift: adjust motion behavior (useful for smoother or more dynamic movement)
- seed: set for reproducible results (-1 for random)
- loras[].path: LoRA identifier or a public safetensors URL
- loras[].scale: LoRA weight (tune per LoRA; start around 0.8–1.0)
Prompting guide (I2V + LoRA)
Write prompts like a director’s brief:
- Subject: who/what is on screen
- Action: what changes over time (gesture, expression, environment)
- Camera: push-in, pull-back, pan, tilt, handheld vs. locked-off
- Mood/lighting: golden hour, neon rain, candlelight, fog, rim light
- Motion style: subtle, smooth, energetic, dramatic
LoRA stacking tip: keep the base prompt clean, and let LoRAs handle style/identity. If multiple LoRAs conflict, lower one LoRA scale rather than overloading the prompt.
Example prompts
- A stylish street cat wearing sunglasses holds a skateboard. The cat shifts its stance, tail sways, and the camera slowly pushes in. Cinematic lighting, crisp detail, smooth natural motion, 720p, 5 seconds.
- A fantasy portrait comes alive: soft wind moves hair and fabric, embers drift across the frame, the character slowly turns their head and smiles. Slow dolly-in, moody lighting, subtle fog, smooth motion.
Wan 2.1 I2v 720p 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/i2v-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 I2v 720p Lora Ultra Fast below.
HTTP example
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/i2v-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",
"image": "https://example.com/your-input.jpg",
"negative_prompt": "blurry, low quality, distorted",
"loras": [
{
"path": "Remade-AI/Squish",
"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].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/i2v-720p-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",
"loras": [
{
"path": "Remade-AI/Squish",
"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 outputPython example
# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/wan-2.1/i2v-720p-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",
"loras": [
{
"path": "Remade-AI/Squish",
"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 I2v 720p Lora Ultra Fast API — Frequently asked questions
What is the Wan 2.1 I2v 720p Lora Ultra Fast API?
Wan 2.1 I2v 720p Lora Ultra Fast is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. WAN 2.1 i2v 720p is an ultra-fast Image-to-Video model that turns images into 720P videos and supports custom LoRAs for style control. 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 I2v 720p 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-i2v-720p-lora-ultra-fast.
How much does Wan 2.1 I2v 720p Lora Ultra Fast cost per run?
Wan 2.1 I2v 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.
What inputs does Wan 2.1 I2v 720p Lora Ultra Fast accept?
Key inputs: `prompt`, `image`, `duration`, `size`, `seed`, `guidance_scale`. 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-i2v-720p-lora-ultra-fast.
How long does Wan 2.1 I2v 720p Lora Ultra Fast take to generate?
Average end-to-end generation time on WaveSpeedAI is around 58 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 I2v 720p 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.