Kling V3 Pro

https://mcp.app.layer.ai/mcp

execute_forge({
  "base_model_id": "kling-v3-pro",
  "prompt": "A torch-lit dungeon corridor, slow dolly forward",
  "batch_size": 1,
  "duration_seconds": 3
})

# Start the generation — returns an inference id straight away.
curl -X POST https://api.app.layer.ai/api/v2/workspaces/$WORKSPACE_ID/base-models/kling-v3-pro/inferences \
  -H "Authorization: Bearer $LAYER_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
        "prompt": "A torch-lit dungeon corridor, slow dolly forward"
      }'

# Poll until status is "complete" — the response then carries the asset URLs.
curl https://api.app.layer.ai/api/v2/workspaces/$WORKSPACE_ID/inferences/$INFERENCE_ID \
  -H "Authorization: Bearer $LAYER_TOKEN"

prompt

arrayrequired

Describe what to generate. Split it into shots to direct each part of the clip.

negative_prompt

string

What to keep out of the result.

aspect_ratio

enumdefault LANDSCAPE_16_9_1080pLANDSCAPE_16_9_1080p, PORTRAIT_9_16_1080p, SQUARE_1080p

The shape and size of the output.

guidance_scale

numberdefault 00–1

How closely the result follows the prompt. Lower gives the model more freedom.

duration_seconds

integerdefault 3

Length of the generated clip, in seconds.

generate_audio

booleandefault false

Add sound, generated with the video in the same pass.

guidance_file_first_frame

file[]

The frame the video starts on

guidance_file_last_frame

file[]

The frame the video ends on

prompt_language

string

The language the prompt is written in, so it reaches the model as you meant it.