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Wan 2.1 T2V 720P LoRA Ultra Fast

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WAN 2.1 Text-to-Video 720P delivers unlimited ultra-fast videos from text prompts and supports custom LoRAs for personalized styles. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

lora-support
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Bereit

$0.225pro Durchlauf·~44 / $10

BeispieleAlle anzeigen

A small vietnamese village is on [r3al_f1re] with many wood houses burning, smoke filling the air, with flames consuming the dry grass and smoke filling the sky above

[actsoonr] movie clip close up of a singing actsoonr, tall thin green-eyed with asymmetrical brown haircut, riding a crimson horse through Leningrad at dusk, next to the river Neva embankment, whilst singing with smug passionate intensity, gesturing dramatically and theatrically, leaning close to the camera, passing next to the fast-flowing Neva river where a sleeping red-haired woman in greenish Soviet army uniform floats over currents nearby, whilst camera zooms in slowly onto actsoonr face looking over, extremely sharp extreme close-up, crisp 8k dramatic action shot, detailed skin. raw details, fast extreme clip, professional footage close up of actsoonr. Extremely detailed visceral cinematography, crisp detailed close up cinematic scene of actsoonr. Crisp 8k UHD DSLR professional color-corrected detailed footage.

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README

Wan 2.1 Text-to-Video 720p LoRA Ultra Fast

Wan 2.1 Text-to-Video 720p LoRA Ultra Fast is a lightning-fast text-to-video generation model with full LoRA support. Generate HD 720p videos from text descriptions in seconds, with custom styles and effects — perfect for rapid iteration and high-volume content creation.

Why It Stands Out

  • Ultra-fast processing: Optimized for speed without sacrificing quality.
  • LoRA support: Apply custom LoRA models for specific styles and effects.
  • HD 720p output: Generate crisp 1280×720 videos with rich detail.
  • Prompt Enhancer: Built-in AI-powered prompt optimization for better results.
  • Negative prompt support: Exclude unwanted elements for cleaner outputs.
  • Fine-tuned control: Adjust guidance scale and flow shift for precise results.
  • Reproducibility: Use the seed parameter to recreate exact results.

Parameters

ParameterRequiredDescription
promptYesText description of the video you want to generate.
negative_promptNoElements to avoid in the output.
lorasNoLoRA models to apply (path and scale).
sizeNoOutput resolution: 1280×720 (default: 1280×720).
num_inference_stepsNoQuality/speed trade-off (default: 30).
durationNoVideo length: 5 or 10 seconds (default: 5).
guidance_scaleNoPrompt adherence strength (default: 5).
flow_shiftNoMotion flow control (default: 5).
seedNoSet for reproducibility; -1 for random.

How to Use

  1. Write a prompt describing the scene, action, and style you want. Use the Prompt Enhancer for AI-assisted optimization.
  2. Add a negative prompt (optional) — specify elements to exclude.
  3. Add LoRAs (optional) — select LoRA models and adjust their scale. Check recommended LoRAs for inspiration.
  4. Set duration — choose 5 or 10 seconds.
  5. Adjust parameters (optional) — fine-tune guidance scale and flow shift.
  6. Click Run and wait for your video to generate.
  7. Preview and download the result.

How to Use LoRA

LoRA (Low-Rank Adaptation) lets you apply custom styles without retraining the full model.

  • Add LoRA: Enter the LoRA path and adjust the scale (0.0–1.0).
  • Recommended LoRAs: Check the interface for suggested LoRAs with preview images (e.g., Fire effects).
  • Scale adjustment: Higher scale means stronger style effect.

Best Use Cases

  • Rapid Prototyping — Quickly test video concepts with custom styles.
  • Visual Effects — Apply effects like fire, water, smoke with specialized LoRAs.
  • Social Media Content — Create stylized videos for TikTok, Reels, and Shorts.
  • Batch Processing — Generate multiple videos efficiently at scale.
  • Creative Exploration — Experiment with different LoRA combinations.

Pricing

DurationPrice
5 seconds$0.225
10 seconds$0.3375

Pro Tips for Best Quality

  • Be detailed in your prompt — describe subject, action, environment, lighting, and mood.
  • Use LoRAs to apply specific visual effects like fire, explosions, or weather.
  • Start with LoRA scale around 0.7–1.0 and adjust based on results.
  • Use negative prompts to reduce artifacts like blur, distortion, or unwanted motion.
  • Check recommended LoRAs for proven style effects.
  • Fix the seed when iterating to compare different LoRA combinations.

Notes

  • Processing time is optimized for speed — expect quick turnaround.
  • Higher num_inference_steps produces better quality but increases generation time.
  • Please ensure your prompts comply with content guidelines.
Hinweis:Diese Website nutzt KI-Modelle von Drittanbietern.

Wan 2.1 T2v 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/t2v-720p-lora-ultra-fast 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 T2v 720p Lora Ultra Fast below.

HTTP example
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",
    "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/t2v-720p-lora-ultra-fast" \
  -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
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/t2v-720p-lora-ultra-fast";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "size": "1280*720",
        "num_inference_steps": 30,
        "duration": 5,
        "guidance_scale": 5,
        "flow_shift": 5,
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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));
}
Python example
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",
    "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/t2v-720p-lora-ultra-fast", 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 T2v 720p Lora Ultra Fast API — Frequently asked questions

What is the Wan 2.1 T2v 720p Lora Ultra Fast API?

Wan 2.1 T2v 720p Lora Ultra Fast is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. WAN 2.1 Text-to-Video 720P delivers unlimited ultra-fast videos from text prompts and supports custom LoRAs for personalized 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 2.1 T2v 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 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-t2v-720p-lora-ultra-fast.

How much does Wan 2.1 T2v 720p Lora Ultra Fast cost per run?

Wan 2.1 T2v 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 T2v 720p 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-720p-lora-ultra-fast.

How do I get started with the Wan 2.1 T2v 720p Lora Ultra Fast API?

Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.

Can I use Wan 2.1 T2v 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.

Wan 2.1 T2V 720P LoRA Ultra Fast | Custom LoRA Image API | WaveSpeedAI