Kwaivgi Kling V2.0 I2V Master
Playground
Try it on WavespeedAI!Kling 2.0 is the latest AI video model from Kling AI that improves upon its predecessor, the Kling 1.6 model. This new release features improved prompt understanding, enhanced character motion dynamics, greater visual quality, and even a ‘Multi-Elements Editor’ that makes video editing more accessible, flexible, and efficient.
Features
Kling 2.0 Key Features
Enhanced Prompt Adherence
Kling 2.0 interprets complex user prompts with sequential actions more precisely than ever. It can accurately understand intricate character actions, delicate expressions, and advanced camera movements, allowing professional-grade control over visual sequences.
Better Motion Dynamics
Kling 2.0 supports a greater motion range with more natural and fluid character movements. It generates immersive sequences simulating complex motions with realistic detail.
Improved Visual Aesthetics
Kling 2.0 produces higher-quality videos featuring richer details and more accurate visual style control. It enables cinematic-level sequences with dramatic expressions and consistent styles using reference images.
Multi-Elements Editor
Kling 2.0 introduces a powerful video editor that allows users to add, swap, or delete video content using text or image inputs. Users can swap elements such as clothing or even entire subjects, enabling greater customization and creative control.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/kwaivgi/kling-v2.0-i2v-master" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"image": "https://d3gnftk2yhz9lr.wavespeed.ai/media/images/1747713619037325077_BGFFEAws.jpeg",
"prompt": "Capture a dynamic, high-speed chase scene featuring two motorcycles navigating through a bustling cityscape at night. The camera alternates between close-up shots of the riders' intense expressions, the glowing neon signs of the city, and the sleek lines of the motorcycles as they weave through traffic. The sound of revving engines and the hum of the city create an exhilarating atmosphere.",
"guidance_scale": 0.5,
"duration": "5"
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| image | string | Yes | - | First frame of the video; Supported image formats include.jpg/.jpeg/.png; The image file size cannot exceed 10MB, and the image resolution should not be less than 300*300px, and the aspect ratio of the image should be between 1:2.5 ~ 2.5:1. | |
| end_image | string | No | - | - | Tail frame of the video; Supported image formats include.jpg/.jpeg/.png; The image file size cannot exceed 10MB, and the image resolution should not be less than 300*300px. |
| prompt | string | Yes | - | Text prompt for generation; Positive text prompt; Cannot exceed 2500 characters. | |
| negative_prompt | string | No | - | Negative text prompt; Cannot exceed 2500 characters. | |
| guidance_scale | number | No | 0.5 | 0.00 ~ 1.00 | The guidance scale to use for the generation. |
| duration | string | No | 5 | 5, 10 | The duration of the generated media in seconds. |
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Query Parameters
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |