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Runway Aleph 2 Video Edit

runwayml/

Runway Aleph 2 Video Edit is an in-context video editing model for precise prompt-based edits, multi-shot consistency, and optional keyframe guidance, supporting 2-30 second input videos and up to 5 keyframes for controlled video modification workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

video-to-video
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就緒

$1.85每次運行

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README

Runway Aleph 2

Runway Aleph 2 edits videos with natural-language instructions while preserving scene consistency. Upload a source video, describe the edit you want, and optionally provide keyframe images to guide specific moments in the edited result.

Why Choose This?

  • Natural-language video editing
    Edit an existing video by describing the desired change in plain text.

  • Scene-consistent edits
    Apply visual changes while preserving the overall structure and continuity of the source video.

  • Multi-shot editing support
    Work with videos that contain multiple shots while maintaining coherent edit behavior.

  • Optional keyframe guidance
    Provide up to 5 reference keyframes to guide the beginning, ending, or specific timestamps of the edited video.

  • Seed control
    Use a seed when you need more reproducible edit results.

Parameters

ParameterRequiredDescription
videoYesSource video to edit. Must be 2–30 seconds and under 16 MB.
promptYesNatural-language instruction describing the edit to apply.
keyframe_imagesNoOptional reference keyframe images. Supports up to 5 images.
keyframe_positionsNoPosition for each keyframe image. Use first, last, or a timestamp in seconds.
seedNoRandom seed for reproducible results.

How to Use

  1. Upload your video — Provide a 2–30 second source video under 16 MB.
  2. Write your edit prompt — Describe the visual change you want to apply.
  3. Add keyframes optional — Upload up to 5 keyframe images when you need stronger guidance for specific moments.
  4. Set keyframe positions optional — Use first, last, or timestamps in seconds to place each keyframe.
  5. Set seed optional — Use a fixed seed when you want more reproducible results.
  6. Submit — Generate the edited video.

Pricing

Pricing is $1.85 per 5 seconds of source video duration, prorated from 2 to 30 seconds. This is equivalent to $0.37 per second.

Video LengthPrice
2s$0.74
5s$1.85
10s$3.70
30s$11.10

Best Use Cases

  • Prompt-based video editing — Apply natural-language edits to existing clips.
  • Scene restyling — Change mood, lighting, atmosphere, or visual direction while preserving scene continuity.
  • Keyframe-guided edits — Guide the first frame, last frame, or specific timestamps with reference images.
  • Multi-shot video edits — Edit clips that contain multiple shots while keeping the result coherent.
  • Creative iteration — Test different edit directions from the same source video.

Pro Tips

  • Use a clear source video within the supported 2–30 second range.
  • Keep the edit prompt focused on the specific change you want.
  • Use keyframe images when the edit needs stronger visual guidance at specific moments.
  • Make sure each keyframe image has a matching keyframe position.
  • Use first or last for simple start/end guidance.
  • Use timestamp positions when a keyframe should guide a specific moment in the video.
  • Use a fixed seed when comparing prompt or keyframe variations.

Note

Keyframe image and position arrays must contain the same number of entries.

提示:本網站部分功能由第三方 AI 模型提供支援。

Aleph 2 API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/runwayml/aleph-2 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 Aleph 2 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",
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/runwayml/aleph-2" \
  -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/runwayml/aleph-2";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "keyframe_images": [],
    "keyframe_positions": []
}

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/runwayml/aleph-2", 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)

Aleph 2 API — Frequently asked questions

What is the Aleph 2 API?

Aleph 2 is a Runwayml model for video editing, exposed as a REST API on WaveSpeedAI. Runway Aleph 2 Video Edit is an in-context video editing model for precise prompt-based edits, multi-shot consistency, and optional keyframe guidance, supporting 2-30 second input videos and up to 5 keyframes for controlled video modification workflows. 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 Aleph 2 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/runwayml/runwayml-aleph-2.

How much does Aleph 2 cost per run?

Aleph 2 starts at $1.85 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 Aleph 2 accept?

Key inputs: `prompt`, `video`, `seed`, `keyframe_images`, `keyframe_positions`. 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/runwayml/runwayml-aleph-2.

How long does Aleph 2 take to generate?

Median end-to-end generation time on WaveSpeedAI is around 221 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 Aleph 2 outputs commercially?

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

Runway Aleph 2 Video Edit API on WaveSpeedAI