Wan 2.1 i2v-720p generates image-to-video outputs at 720p and supports custom LoRA adapters for personalized styles and fine-tuning. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
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$0.3실행당·~33 / $10
The video begins with a lego woman. A hydraulic press positioned above slowly descends towards the woman. Upon contact, the hydraulic press c5us4 crushes it, deforming and flattening the woman, causing the woman to collapse inward until the woman is no longer recognizable.
Swirling stars accelerate into meteor shower, 3D oil brush strokes flowing with golden particles, 24fps art animation
three beautiful happy women walking towards the camera in a natural way.
Push-in camera, Instantly rocketing towards the heart of the lavender field, the vibrant purple blooms blurring into a hypnotic, swirling vortex of color as the rows of lavender, each individual flower a tiny point of light, rush towards the viewer, the distant cypress trees transforming into sharp, dark silhouettes against a breathtaking sunset sky, the soft golden light illuminating every minute detail of the scene, until the camera slams into the heart of the field, revealing the intricate texture of the blossoms, the delicate variations in purple hues, and the subtle golden undertones of the setting sun, in breathtaking, hyper-real clarity.
Wan 2.1 Image-to-Video 720p LoRA is a powerful image-to-video generation model that transforms static images into dynamic 720p HD videos. With full LoRA support, apply custom styles, artistic effects, or consistent character appearances to create unique animated content.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Source image to animate (upload or public URL). |
| prompt | Yes | Text description of desired motion and style. |
| negative_prompt | No | Elements to avoid in the output. |
| loras | No | LoRA models to apply (path and scale). |
| size | No | Output resolution (default: 1280×720). |
| num_inference_steps | No | Quality/speed trade-off (default: 30). |
| duration | No | Video length: 5 or 10 seconds (default: 5). |
| guidance_scale | No | Prompt adherence strength (default: 5). |
| flow_shift | No | Motion flow control (default: 5). |
| seed | No | Set for reproducibility; -1 for random. |
LoRA (Low-Rank Adaptation) lets you apply custom styles without retraining the full model.
| Duration | Price |
|---|---|
| 5 seconds | $0.30 |
| 10 seconds | $0.45 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/i2v-720p-lora with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Wan 2.1 I2v 720p Lora below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"size": "1280*720",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 5,
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/i2v-720p-lora" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/i2v-720p-lora";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"size": "1280*720",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 5,
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"size": "1280*720",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 5,
"seed": -1
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/i2v-720p-lora", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Wan 2.1 I2v 720p Lora is a WaveSpeedAI model for video generation from images, exposed as a REST API on WaveSpeedAI. Wan 2.1 i2v-720p generates image-to-video outputs at 720p and supports custom LoRA adapters for personalized styles and fine-tuning. 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-i2v-720p-lora.
Wan 2.1 I2v 720p Lora starts at $0.30 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`, `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.
Median end-to-end generation time on WaveSpeedAI is around 162 seconds per request, based on recent successful runs. Queue time varies 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.