Kwaivgi Kling V1.6 I2V Standard API Documentation

Kwaivgi Kling V1.6 I2V Standard API Documentation

Playground

Kling 1.6 is an Image-to-Video model with 195% improvement over 1.5, with improved prompt understanding, physics and visual effects. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Kling V1.6 Image-to-Video Standard is Kuaishou’s reliable image-to-video generation model that transforms static images into dynamic videos with smooth, natural motion. Upload an image, describe the action, and watch your photo come to life.

Why It Stands Out

• Image-driven generation: Animate any image while preserving its original style and composition.
• Prompt-guided motion: Describe the action you want and the model brings it to life.
• Prompt Enhancer: Built-in AI-powered prompt optimization for better results.
• Negative prompt support: Exclude unwanted elements for cleaner outputs.
• Guidance control: Adjust how closely the output follows your prompt.
• Flexible duration: Choose video length based on your needs.

Parameters

How to Use

1. Upload your source image — drag and drop a file or paste a public URL.
2. Write a prompt describing the motion you want. Use the Prompt Enhancer for AI-assisted optimization.
3. Add a negative prompt (optional) — specify elements to exclude.
4. Adjust guidance scale — higher values follow prompts more strictly.
5. Set duration — choose how long you want the video to be.
6. Click Run and wait for your video to generate.
7. Preview and download the result.

Best Use Cases

• Portrait Animation — Bring portrait photos to life with natural movement.
• Social Media Content — Turn photos into engaging video posts.
• Marketing & Advertising — Animate product images and hero shots.
• E-commerce — Create dynamic product showcases from static photography.
• Creative Projects — Animate artwork, illustrations, and personal photos.

Pricing

Pro Tips for Best Quality

• Use high-resolution, well-lit source images for optimal results.
• Keep prompts simple and focused on the main action (e.g., “The woman walks forward”).
• Use lower guidance scale (0.3–0.5) for more natural motion.
• Use higher guidance scale (0.6–0.8) for stronger prompt adherence.
• Use negative prompts to reduce artifacts like blur or distortion.

Notes

• Ensure uploaded image URLs are publicly accessible.
• Processing time varies based on duration and current queue load.
• Please ensure your prompts comply with content guidelines.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
  "guidance_scale": 0.5,
  "duration": 5
}
JSON
)

# 1. Submit the prediction once. Never retry this request automatically:
# a retry after an unclear failure can create a second, billed task.
CHECK='The task may already exist. Check History or the predictions list before submitting again.'
if ! SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  --connect-timeout 10 --max-time 60 \
  -X POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-v1.6-i2v-standard" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}"); then
  printf 'Submit failed. %s\n' "${CHECK}" >&2
  exit 1
fi

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id. %s\n' "${CHECK}" >&2
  exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"

# 2. Poll until the prediction reaches a final status.
BODY_FILE=$(mktemp)
trap 'rm -f "${BODY_FILE}"' EXIT
while true; do
  # Transient errors (429, 5xx) are retried, honoring Retry-After.
  HTTP_CODE=$(curl --silent --show-error --fail-with-body \
    --connect-timeout 10 --max-time 60 --retry 5 --retry-delay 2 \
    --output "${BODY_FILE}" --write-out '%{http_code}' \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}" || true)
  case "${HTTP_CODE}" in
    2??) ;;
    000|429|5??)  # dropped connection or temporary error: keep polling
      printf 'Task %s: HTTP %s, polling again in 10s\n' "${PREDICTION_ID}" "${HTTP_CODE}" >&2
      sleep 10; continue ;;
    *) printf 'Task %s: HTTP %s\n' "${PREDICTION_ID}" "${HTTP_CODE}" >&2
      cat "${BODY_FILE}" >&2; exit 1 ;;
  esac
  RESULT=$(jq 'if type == "object" and has("data") then .data else . end' "${BODY_FILE}")
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted)
      printf 'Task %s ended with status %s\n' "${PREDICTION_ID}" "${STATUS}" >&2
      printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done

Parameters

Task Submission Parameters

Request Parameters

Every model also accepts the global request options enable_sync_mode and enable_base64_output; see their pages for details.

Response Parameters

Result Request Parameters

Result Response Parameters