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SAM3 Video is a unified foundation model for prompt-based video segmentation. Provide text, point, box, or mask prompts and the model segments and tracks targets across frames with strong temporal consistency. Supports concept-level (“segment anything with concepts”) and multi-object masks for editing, analytics, and VFX. Ready-to-use REST inference API with fast response, no cold starts, and affordable pricing.

video-to-video
Input

Idle

$0.05per run·~20 / $1

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ExamplesView all

The woman

The girl

The perfume

The woman

Related Models

README

WaveSpeedAI SAM3 Video Video-to-Video

SAM3 Video (wavespeed-ai/sam3-video) is a prompt-based video segmentation and mask-guided editing model. You provide a video plus a short text instruction (and optionally enable mask application), and the model segments/targets the requested subject(s) across frames with strong temporal consistency.

It’s a practical fit for object-focused video edits like background cleanup, removing unwanted elements, or isolating subjects for downstream compositing—especially on short-to-medium clips with clear subjects.

Key capabilities

  • Prompt-based target selection (concept prompts) Identify what to edit/segment using natural language (e.g., “the woman”, “person”, “red car”) without manually drawing masks frame-by-frame.

  • Multi-object targeting in one run Track multiple object categories by listing them in the prompt (comma-separated), producing consistent targets across frames.

  • Mask-guided region control via apply_mask Toggle whether the model applies the mask to the video output for tighter, more controllable edits.

  • Temporal consistency for video workflows Designed to keep results stable across frames, reducing flicker/drift compared with per-frame processing.

  • Editing-oriented use cases Works well for object removal and background cleanup when your prompt clearly specifies what should change and what should stay.

Parameters and how to use

  • video: (required) Input video file or a public URL.
  • prompt: (required) Text instruction for segmentation/editing. Use commas to target multiple objects (e.g., person, cloth).
  • apply_mask: Whether to apply the mask to the video (boolean). Default: true.

Prompt

Write prompts like you’re describing what to target and (if applicable) what the edit intent is.

Tips:

  • Prefer short, concrete nouns for targeting: person, woman, car, dog, shirt.
  • For multiple targets, use comma-separated labels: person, backpack, bicycle.
  • If you’re doing cleanup/removal, include keep-constraints to preserve look: “remove the person in the background, keep lighting unchanged”

Examples:

  • The woman
  • person, cloth
  • remove the person in the background, keep lighting unchanged

Media (Videos)

  • Provide video as either:

  • an uploaded file, or

  • a public URL the service can fetch.

  • Pricing/processing uses a billed duration clamp of 5–600 seconds, so very short clips are billed as 5s, and very long clips are treated as 600s.

Other parameters

  • apply_mask

  • true: apply the model’s mask to the output video (recommended when you want tighter control over the edited region).

  • false: run without applying the mask (useful when you want the model’s edits without explicit masking).

After you finish configuring the parameters, click Run, preview the result, and iterate if needed.

Pricing

Per-run cost depends on video duration (billed duration is clamped to 5–600 seconds), charged in 5-second units at $0.05 per 5s.

Billed durationCost per run
5s$0.05
10s$0.10
600s (max)$6.00

Notes

  • Best results come from stable footage with clear subject separation and minimal heavy motion blur.
  • Turn on apply_mask when you need more precise, localized control (especially in cluttered scenes).
  • If results drift or pick the wrong target, refine the prompt (more specific noun/descriptor) or reduce to fewer targets per run.

Related Models

Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Sam3 Video API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/sam3-video 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 Sam3 Video 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",
    "apply_mask": true
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/sam3-video" \
  -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/sam3-video";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "apply_mask": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "apply_mask": True
}

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/sam3-video", 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)

Sam3 Video API — Frequently asked questions

What is the Sam3 Video API?

Sam3 Video is a WaveSpeedAI model for video editing, exposed as a REST API on WaveSpeedAI. SAM3 Video is a unified foundation model for prompt-based video segmentation. Provide text, point, box, or mask prompts and the model segments and tracks targets across frames with strong temporal consistency. Supports concept-level (“segment anything with concepts”) and multi-object masks for editing, analytics, and VFX. Ready-to-use REST inference API with fast response, no cold starts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Sam3 Video 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/sam3-video.

How much does Sam3 Video cost per run?

Sam3 Video 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 Sam3 Video accept?

Key inputs: `prompt`, `video`, `apply_mask`. 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/sam3-video.

How long does Sam3 Video take to generate?

Median end-to-end generation time on WaveSpeedAI is around 37 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 Sam3 Video 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.

SAM3 Video | AI Video Segmentation API on WaveSpeedAI