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Flashvsr

wavespeed-ai /

FlashVSR is a fast, high-quality video upscaler that boosts resolution and restores clarity for low-resolution or blurry footage. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

upscaler
इनपुट

निष्क्रिय

$0.05प्रति रन·~20 / $1

उदाहरणसभी देखें

संबंधित मॉडल

README

FlashVSR

Upscale videos to 720p, 1080p, 2K, or 4K with a simple upload. This is the most advanced video upscaler in the world, delivering the highest output quality and exceptional frame-to-frame consistency. For the best quality, try Ultimate Video Upscaler or Video Upscaler Pro. If you want a cheaper option, try our Standard Video Upscaler instead. No local setup required. Also, for Sora-2 wartermark removing, try Watermark Remover!

Why it looks great

  • Temporal consistency: minimizes flicker and ghosting across frames for stable motion.
  • Detail reconstruction: restores fine textures (hair, fabric, foliage) and sharp edges without over-sharpening.
  • Artifact cleanup: reduces compression blocks, ringing, and shimmering in challenging shots.
  • Motion-aware upscaling: preserves fast action and camera pans with fewer motion artifacts.
  • Natural look: balances perceptual quality with crispness to avoid plastic or overprocessed outputs.

Limits and Performance

  • Max clip length per job: up to 10 minutes
  • Processing speed: approximately 3–20 seconds of wall time to process 1 second of video (varies by resolution and queue load)

Pricing

ResolutionCost per 5 seconds (USD)
4K$0.16
2K$0.12
1080p$0.09
720p$0.06

Billing Rules

  • Minimum charge: 5 seconds
  • Per-second rate = (price per 5 seconds) ÷ 5
  • Billed duration = video length in seconds (rounded up), with a 5-second minimum
  • Total cost = billed duration × per-second rate (by output resolution)

How to Use

  1. Choose the target resolution and parameters.
  2. Upload your video (≤ 10 minutes).
  3. Submit the job and wait for processing.
  4. Preview and download the result.

Pro tips for best quality

  • Upload the highest-quality source you have; avoid heavily compressed inputs when possible.
  • Keep original frame rate; avoid unnecessary re-encoding before upload.
  • Pick the lowest resolution that meets your delivery needs (1080p = speed/cost, 2K/4K = maximum detail).
  • For long videos, process in segments to parallelize and then merge.

Notes

  • Actual processing time may vary based on resolution, model choice, and current queue.
  • For videos longer than 10 minutes, split into multiple segments, process separately, and merge afterward.
नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है।

Flashvsr API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flashvsr 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 Flashvsr below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "target_resolution": "1080p"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flashvsr" \
  -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/flashvsr";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "target_resolution": "1080p"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "target_resolution": "1080p"
}

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/flashvsr", 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)

Flashvsr API — Frequently asked questions

What is the Flashvsr API?

Flashvsr is a WaveSpeedAI model for upscaling, exposed as a REST API on WaveSpeedAI. FlashVSR is a fast, high-quality video upscaler that boosts resolution and restores clarity for low-resolution or blurry footage. 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 Flashvsr 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/flashvsr.

How much does Flashvsr cost per run?

Flashvsr starts at $0.050 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 Flashvsr accept?

Key inputs: `video`, `target_resolution`. 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/flashvsr.

How long does Flashvsr take to generate?

Median end-to-end generation time on WaveSpeedAI is around 75 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Flashvsr 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.

Flashvsr | AI Video Upscaler API | WaveSpeedAI