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Clarity AI Crystal Video Upscaler is a fast AI video super-resolution model that increases video resolution with target megapixel control and Clarity AI crystal-video processing. Ready-to-use REST inference API for enhancing low-resolution videos, restoring details, improving visual clarity, upscaling creative clips, product videos, social media content, and professional video enhancement workflows with simple integration, no coldstarts, and affordable pricing.

upscaler
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$0.1cho mỗi lần chạy·~10 / $1

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README

Clarity AI Crystal Video Upscaler

Clarity AI Crystal Video Upscaler enhances and enlarges video content with a simple megapixel-based target control. Upload a source video, choose the desired target megapixels, and generate a cleaner, sharper upscaled result for higher-quality delivery, presentation, or archival workflows.

Why Choose This?

  • Video upscaling workflow Improve the visual quality of existing video content with a simple upload-and-upscale process.

  • Megapixel-based output control Use target_megapixels to choose the intended output size more directly.

  • Simple pricing logic Cost scales with both target megapixels and video duration, with a minimum charge for very small jobs.

  • Suitable for restoration and delivery Useful for sharpening low-resolution clips for presentations, publishing, or asset reuse.

  • Production-ready API Suitable for enhancement pipelines, archival cleanup, and commercial video preparation workflows.

Parameters

ParameterRequiredDescription
videoYesInput video to upscale.
target_megapixelsNoTarget output size in megapixels. Higher values produce larger and more detailed upscaled frames.

How to Use

  1. Upload your video — provide the source video you want to enhance.
  2. Choose target megapixels — set the desired output size based on your delivery needs.
  3. Submit — run the model and download the upscaled video.

Example Use Case

Upscale a low-resolution talk-show or interview clip to a cleaner, sharper version for reuse in presentations, publishing, or social distribution.

Pricing

Pricing is based on video duration and target_megapixels.

Billing Rules

  • Base price is $0.10
  • Standard rate is $0.10 × target_megapixels × video duration (seconds)
  • The final charge is the greater of:
    • $0.10 minimum
    • $0.10 × target_megapixels × duration

Example Costs

Target Megapixels1s5s10s
1 MP$0.10$0.50$1.00
2 MP$0.20$1.00$2.00
4 MP$0.40$2.00$4.00
8 MP$0.80$4.00$8.00

Best Use Cases

  • Low-resolution video enhancement — Improve the clarity of older or compressed video clips.
  • Presentation and publishing prep — Generate cleaner upscaled outputs for decks, demos, and public distribution.
  • Archival restoration workflows — Prepare sharper versions of legacy footage for reuse.
  • Commercial asset improvement — Upgrade visual quality for marketing, social, and branded content.
  • General video cleanup — Increase perceived quality for clips that need a higher-resolution presentation.

Pro Tips

  • Start with a lower target_megapixels value first to validate cost and output quality.
  • Use clean source video whenever possible for better enhancement results.
  • Higher megapixel targets can increase cost quickly on longer clips.
  • Short clips are a good way to test the workflow before processing longer footage.

Notes

  • video is required.
  • Pricing depends on both target_megapixels and source video duration.
  • A minimum charge of $0.10 applies.
  • Longer videos and larger target sizes increase cost proportionally.

Related Models

  • Other Clarity AI upscaling and enhancement models may be useful when you need image-first workflows or different restoration trade-offs.
Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp. Giá trong tài liệu chỉ để tham khảo và có thể đã lỗi thời. Nút Generate hiển thị giá ước tính; phí cuối cùng của tác vụ sẽ được áp dụng.

Crystal Video Upscaler API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/clarity-ai/crystal-video-upscaler 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 Crystal Video Upscaler 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_megapixels": 2
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/clarity-ai/crystal-video-upscaler" \
  -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/clarity-ai/crystal-video-upscaler";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "target_megapixels": 2
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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_megapixels": 2
}

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/clarity-ai/crystal-video-upscaler", 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)

Crystal Video Upscaler API — Frequently asked questions

What is the Crystal Video Upscaler API?

Crystal Video Upscaler is a Clarity model for upscaling, exposed as a REST API on WaveSpeedAI. Clarity AI Crystal Video Upscaler is a fast AI video super-resolution model that increases video resolution with target megapixel control and Clarity AI crystal-video processing. Ready-to-use REST inference API for enhancing low-resolution videos, restoring details, improving visual clarity, upscaling creative clips, product videos, social media content, and professional video enhancement workflows with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Crystal Video Upscaler 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/clarity-ai/clarity-ai-crystal-video-upscaler.

How much does Crystal Video Upscaler cost per run?

Crystal Video Upscaler starts at $0.10 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 Crystal Video Upscaler accept?

Key inputs: `video`, `target_megapixels`. 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/clarity-ai/clarity-ai-crystal-video-upscaler.

How long does Crystal Video Upscaler take to generate?

Median end-to-end generation time on WaveSpeedAI is around 122 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 Crystal Video Upscaler outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Clarity). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Crystal Video Upscaler | AI Video Upscaler API on WaveSpeedAI