KlingAI 1.0 Pro
KlingAI 1.0 Pro is a video generation model for demanding creators. It improves motion quality with smoother movement. It refines lighting control for more realistic scenes. It delivers sharper visual detail compared to the standard Kling 1.0 model for higher quality clips.
API Options
Platform-level options for task execution and delivery.
-
taskType
string required value: videoInference -
Identifier for the type of task being performed
-
taskUUID
string required UUID v4 -
UUID v4 identifier for tracking tasks and matching async responses. Must be unique per task.
-
outputType
string default: URL -
Video output type.
Allowed values 1 value
-
outputFormat
string default: MP4 -
Specifies the file format of the generated output. The available values depend on the task type and the specific model's capabilities.
- `MP4`: Widely supported video container (H.264), recommended for general use.
- `WEBM`: Optimized for web delivery.
- `MOV`: QuickTime format, common in professional workflows (Apple ecosystem).
Allowed values 3 values
-
outputQuality
integer min: 20 max: 99 default: 95 -
Compression quality of the output. Higher values preserve quality but increase file size.
-
webhookURL
string URI -
Specifies a webhook URL where JSON responses will be sent via HTTP POST when generation tasks complete. For batch requests with multiple results, each completed item triggers a separate webhook call as it becomes available.
Learn more 1 resource
- Webhooks PLATFORM
- Webhooks
-
deliveryMethod
string default: async -
Determines how the API delivers task results.
Allowed values 1 value
- Returns an immediate acknowledgment with the task UUID. Poll for results using getResponse. Required for long-running tasks like video generation.
Learn more 1 resource
- Task Polling PLATFORM
-
uploadEndpoint
string URI -
Specifies a URL where the generated content will be automatically uploaded using the HTTP PUT method. The raw binary data of the media file is sent directly as the request body. For secure uploads to cloud storage, use presigned URLs that include temporary authentication credentials.
Common use cases:
- Cloud storage: Upload directly to S3 buckets, Google Cloud Storage, or Azure Blob Storage using presigned URLs.
- CDN integration: Upload to content delivery networks for immediate distribution.
// S3 presigned URL for secure upload https://your-bucket.s3.amazonaws.com/generated/content.mp4?X-Amz-Signature=abc123&X-Amz-Expires=3600 // Google Cloud Storage presigned URL https://storage.googleapis.com/your-bucket/content.jpg?X-Goog-Signature=xyz789 // Custom storage endpoint https://storage.example.com/uploads/generated-image.jpgThe content data will be sent as the request body to the specified URL when generation is complete.
-
safety
object -
Content safety checking configuration for video generation.
Properties 2 properties
-
safety»checkContentcheckContent
boolean default: false -
Enable or disable content safety checking. When enabled, defaults to
fastmode.
-
safety»modemode
string default: none -
Safety checking mode for video generation.
Allowed values 3 values
- Disables checking.
- Checks key frames.
- Checks all frames.
-
-
ttl
integer min: 60 -
Time-to-live (TTL) in seconds for generated content. Only applies when
outputTypeisURL.
-
includeCost
boolean default: false -
Include task cost in the response.
-
numberResults
integer min: 1 max: 4 default: 1 -
Number of results to generate. Each result uses a different seed, producing variations of the same parameters.
Inputs
Input resources for the task (images, audio, etc). These must be nested inside the inputs object.
inputs object.-
inputs»frameImagesframeImages
array of strings or objects min items: 1max items: 2 -
An array of frame-specific image inputs to guide video generation. Each item can be either a plain image input (UUID, URL, Data URI, or Base64) or an object that pairs an image with a target frame position.
The
frameImagesparameter allows you to constrain specific frames within the video sequence, ensuring that particular visual content appears at designated points. This is different fromreferenceImages, which provide overall visual guidance without constraining specific timeline positions.When the
frameparameter is omitted, automatic distribution rules apply:- 1 image: Used as the first frame.
- 2 images: First and last frames.
Examples 3 examples
Shorthand format: When you don't need to specify a frame position, you can pass a plain image input directly.
"frameImages": [ "aac49721-1964-481a-ae78-8a4e29b91402" ]Object format: When you need to specify a frame position, use an object with
imageandframe.First and last frames: With two images, they automatically become the first and last frames of the video sequence. You can mix shorthand and object formats."frameImages": [ { "image": "aac49721-1964-481a-ae78-8a4e29b91402", "frame": "first" } ]"frameImages": [ "aac49721-1964-481a-ae78-8a4e29b91402", { "image": "3ad204c3-a9de-4963-8a1a-c3911e3afafe", "frame": "last" } ]Format 1: string[]
-
Image input (UUID, URL, Data URI, or Base64).
Format 2: object[] 2 properties
-
inputs»frameImages»imageimage
string required -
Image input (UUID, URL, Data URI, or Base64).
-
inputs»frameImages»frameframe
object -
Target frame position for the image. Supports first and last frame.
Allowed values 4 values
- First frame of the video.
- Last frame of the video.
- Frame index 0 (first frame).
- Frame index -1 (last frame).
Generation Parameters
Core parameters for controlling the generated content.
-
model
string required value: klingai:1@2 -
Identifier of the model to use for generation.
Learn more 3 resources
-
positivePrompt
string required min: 2 max: 2500 -
Text prompt describing elements to include in the generated output.
Learn more 2 resources
-
negativePrompt
string min: 2 max: 2500 -
Prompt to guide what to exclude from generation. Ignored when guidance is disabled (CFGScale ≤ 1).
Learn more 1 resource
-
width
integer paired with height -
Width of the generated media in pixels.
Learn more 2 resources
-
height
integer paired with width -
Height of the generated media in pixels.
Learn more 2 resources
-
duration
float default: 5 -
Length of the generated video in seconds. The total number of frames produced is determined by duration multiplied by the model's frame rate (fps).
Allowed values 2 values
-
CFGScale
float min: 0 max: 1 step: 0.01 default: 0.5 -
Guidance scale representing how closely the output will resemble the prompt. Higher values produce results more aligned with the prompt.
Learn more 1 resource
Amber Monorail Over Ricefields
{
"taskType": "videoInference",
"taskUUID": "1aabf0e3-0470-4545-a85f-6f2f41bea1ce",
"model": "klingai:1@2",
"positivePrompt": "Transform the provided first-frame image into a polished cinematic video. The elevated monorail continues forward in a gentle arc above reflective rice terraces while faint mist drifts between the steps, grasses ripple in the breeze, and water surfaces shimmer with subtle moving highlights. Keep the warm window lights consistent as the sky deepens from cobalt to indigo, with delicate cloud movement over distant volcanic hills. Emphasize smooth camera motion, realistic environmental movement, sharp structural details, natural reflections, and nuanced evening light for a serene rural futurist atmosphere.",
"negativePrompt": "flicker, jitter, warped train geometry, duplicated people, sudden lighting shifts, low detail, oversaturated colors, text, watermark, logo, deformed landscape, chaotic motion, blurry frames",
"width": 1280,
"height": 720,
"duration": 5,
"CFGScale": 0.5,
"inputs": {
"frameImages": [
{
"image": "https://assets.runware.ai/assets/inputs/a1b9a2f6-c1f4-4350-99af-0f1edeff98de.jpg",
"frame": "first"
}
]
}
}{
"taskType": "videoInference",
"taskUUID": "1aabf0e3-0470-4545-a85f-6f2f41bea1ce",
"videoUUID": "0f31f99f-d78a-4ebd-98d6-80cbc7311df9",
"videoURL": "https://vm.runware.ai/video/os/a10d08/ws/5/vi/0f31f99f-d78a-4ebd-98d6-80cbc7311df9.mp4",
"seed": 1510921402,
"cost": 0.3234
}Wind-Torn Lighthouse Interior
{
"taskType": "videoInference",
"taskUUID": "fba99a8e-d47b-4a7f-8a8c-01cf1a7e6863",
"model": "klingai:1@2",
"positivePrompt": "Animate this single establishing frame into a realistic cinematic sequence inside an abandoned lighthouse. The camera slowly pushes forward from the stair landing and subtly arcs to the right. Dust motes drift through angled dawn light, torn paper maps flutter gently, the hanging lamp sways slightly, the great lens rotates with soft mechanical inertia, distant sea haze shifts beyond the windows, and faint floor debris stirs in intermittent gusts. Emphasize smooth natural motion, refined lighting transitions, crisp texture detail in peeling paint, worn wood, brass, and glass, atmospheric depth, and a contemplative maritime mood.",
"negativePrompt": "jittery motion, warped geometry, flicker, low detail, duplicated objects, sudden camera jumps, oversaturated colors, cartoon style, extra windows, broken anatomy, text, watermark, logo",
"width": 1280,
"height": 720,
"duration": 5,
"CFGScale": 0.6,
"inputs": {
"frameImages": [
{
"image": "https://assets.runware.ai/assets/inputs/dc758e36-53d0-4916-9314-1f2cd0603756.jpg",
"frame": "first"
}
]
}
}{
"taskType": "videoInference",
"taskUUID": "fba99a8e-d47b-4a7f-8a8c-01cf1a7e6863",
"videoUUID": "3551dca3-5e90-4e56-9a81-bdeafbbd8eae",
"videoURL": "https://vm.runware.ai/video/os/a01d21/ws/5/vi/3551dca3-5e90-4e56-9a81-bdeafbbd8eae.mp4",
"seed": 16462552,
"cost": 0.3234
}Glacial Lantern Train Interior
{
"taskType": "videoInference",
"taskUUID": "4acfb2b1-7cfd-4454-a408-527062cdf1d5",
"model": "klingai:1@2",
"positivePrompt": "Animate this single establishing frame into a refined cinematic shot inside an ornate vintage train carriage moving through a frozen landscape. The camera gently pushes forward down the aisle with slight natural sway from the train. The cartographer subtly breathes, lowers their gaze to the glowing map, and the map edges flutter softly. Lantern light flickers warmly across brass fixtures and polished wood while cold blue light shifts through the frosted windows. Outside, massive ice formations glide past with believable parallax. Fine details remain sharp: condensation trails, fabric texture, reflections in the glass, drifting dust motes, and delicate vibration of straps and hanging objects. Emphasize smooth motion, realistic lighting transitions, elegant atmosphere, and premium cinematic clarity.",
"negativePrompt": "jittery motion, warped anatomy, duplicate limbs, deformed hands, melting objects, flickering face, unstable composition, abrupt camera movement, overexposure, oversaturation, low detail, blurry windows, noisy shadows, text, watermark, logo",
"width": 1280,
"height": 720,
"duration": 5,
"CFGScale": 0.5,
"inputs": {
"frameImages": [
{
"image": "https://assets.runware.ai/assets/inputs/c1d36e61-ea50-4005-9d53-a047fcee3502.jpg",
"frame": "first"
}
]
}
}{
"taskType": "videoInference",
"taskUUID": "4acfb2b1-7cfd-4454-a408-527062cdf1d5",
"videoUUID": "14139f1d-5462-4226-8c2f-256bea3f24c0",
"videoURL": "https://vm.runware.ai/video/os/a14d18/ws/5/vi/14139f1d-5462-4226-8c2f-256bea3f24c0.mp4",
"seed": 343759054,
"cost": 0.3234
}Lantern Tram Through Floodplain
{
"taskType": "videoInference",
"taskUUID": "ce780267-bf05-44d0-a469-d05de624e080",
"model": "klingai:1@2",
"positivePrompt": "A cinematic wide shot of an antique electric tram moving slowly along narrow raised tracks through a vast flooded grassland settlement at blue hour. Warm paper lanterns hang from the tram roof and reflect in shallow water between reeds. Stilt houses, fishing platforms, and distant wind-driven water wheels line the horizon. The camera begins low near rippling water and drifts sideways with the tram, revealing passengers in silhouette, swaying lantern light, small birds lifting off from the reeds, and gentle wake patterns spreading outward. Realistic atmosphere, intricate textures, polished metal, damp wood, layered depth, subtle mist near the water surface, natural color contrast between cool sky and warm lantern glow, refined lighting control, smooth continuous motion, high detail, cinematic composition.",
"negativePrompt": "text overlays, watermark, logo, low detail, flicker, jitter, warped anatomy, duplicated passengers, broken tram geometry, oversaturated colors, harsh contrast, blurry background, camera shake, glitch artifacts",
"width": 1280,
"height": 720,
"duration": 10,
"CFGScale": 0.7
}{
"taskType": "videoInference",
"taskUUID": "ce780267-bf05-44d0-a469-d05de624e080",
"videoUUID": "71c1ea7d-7e81-467e-bb82-f807667cf845",
"videoURL": "https://vm.runware.ai/video/os/a17d13/ws/5/vi/71c1ea7d-7e81-467e-bb82-f807667cf845.mp4",
"seed": 941904090,
"cost": 0.6468
}Windblown Tundra Beacon Arrival
{
"taskType": "videoInference",
"taskUUID": "45c38f9b-20fe-4748-8b10-f386fc782148",
"model": "klingai:1@2",
"positivePrompt": "A cinematic aerial-to-mid-height view across a frozen tundra, transitioning from a quiet pre-sunrise approach to an activated polar beacon station. The scene begins with dim blue ambient light, light ground fog, wind carving trails through snow, and a small expedition vehicle crossing the plain toward a tall beacon tower. As the shot progresses, the camera glides forward and slightly downward, snow plumes sweep across the foreground, flags snap in the wind, antenna rings begin rotating, amber energy coils awaken along the tower, and the horizon warms into a vivid sunrise glow. Emphasize smooth environmental motion, realistic lighting changes, crisp texture detail in snow and metal, believable vehicle movement, and a majestic remote atmosphere.",
"negativePrompt": "text overlays, logo, watermark, low detail, blurry frames, flicker, deformed structures, duplicate vehicle, jittery camera, oversaturated colors, cartoon style, floating objects, broken perspective, noisy image, crowd, city elements",
"width": 1280,
"height": 720,
"duration": 5,
"CFGScale": 0.5,
"inputs": {
"frameImages": [
{
"image": "https://assets.runware.ai/assets/inputs/8acdd371-f7c2-445d-a943-a16f9c8ceab9.jpg",
"frame": "first"
},
{
"image": "https://assets.runware.ai/assets/inputs/d4ecd49c-368d-4ef7-800b-9bcff5df0a28.jpg",
"frame": "last"
}
]
}
}{
"taskType": "videoInference",
"taskUUID": "45c38f9b-20fe-4748-8b10-f386fc782148",
"videoUUID": "c24b35cf-27be-4f5b-9040-16351814a104",
"videoURL": "https://vm.runware.ai/video/os/a24d12/ws/5/vi/c24b35cf-27be-4f5b-9040-16351814a104.mp4",
"seed": 2103885327,
"cost": 0.3234
}