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.
निष्क्रिय
$0.1प्रति रन·~10 / $1
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.
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.
| Parameter | Required | Description |
|---|---|---|
| video | Yes | Input video to upscale. |
| target_megapixels | No | Target output size in megapixels. Higher values produce larger and more detailed upscaled frames. |
Upscale a low-resolution talk-show or interview clip to a cleaner, sharper version for reuse in presentations, publishing, or social distribution.
Pricing is based on video duration and target_megapixels.
| Target Megapixels | 1s | 5s | 10s |
|---|---|---|---|
| 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 | $8.00 |
target_megapixels value first to validate cost and output quality.video is required.target_megapixels and source video duration.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/clarity-ai/crystal-video-upscaler with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Crystal Video Upscaler below.
set -euo pipefail
: "${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
doneconst 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));
}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 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.
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.
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.
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.
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.
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.