# v3-pro/text-to-video {% columns %} {% column width="66.66666666666666%" %} {% hint style="info" %} This documentation is valid for the following list of our models: * `klingai/video-v3-pro-text-to-video` {% endhint %} {% endcolumn %} {% column width="33.33333333333334%" %} Try in Playground {% endcolumn %} {% endcolumns %} High-quality text-to-video generation with cinematic visuals, smooth motion, built-in audio generation, and support for multi-shot scenes. ## Setup your API Key If you don’t have an API key for the AI/ML API yet, feel free to use our [Quickstart guide](https://docs.aimlapi.com/quickstart/setting-up). ## How to Make a Call
Step-by-Step Instructions Generating a video using this model involves sequentially calling two endpoints: * The first one is for creating and sending a video generation task to the server (returns a generation ID). * The second one is for requesting the generated video from the server using the generation ID received from the first endpoint. Below, you can find both corresponding API schemas.
## API Schemas ### Create a video generation task and send it to the server This endpoint creates and sends a video generation task to the server — and returns its ID. ## POST /v2/video/generations > ```json {"openapi":"3.0.0","info":{"title":"AIML API","version":"1.0.0"},"servers":[{"url":"https://api.aimlapi.com"}],"paths":{"/v2/video/generations":{"post":{"operationId":"_v2_video_generations","requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"model":{"type":"string","enum":["klingai/video-v3-pro-text-to-video"]},"prompt":{"type":"string","description":"Text prompt for video generation. Either prompt or multi_prompt must be provided, but not both."},"multi_prompt":{"type":"array","items":{"type":"string"},"description":"List of prompts for multi-shot video generation. If provided, overrides the single prompt and divides the video into multiple shots with specified prompts and durations."},"aspect_ratio":{"type":"string","enum":["16:9","9:16","1:1"],"default":"16:9","description":"The aspect ratio of the generated video."},"duration":{"type":"integer","description":"The length of the output video in seconds.","enum":[3,4,5,6,7,8,9,10,11,12,13,14,15],"default":"5"},"shot_type":{"type":"string","enum":["customize","intelligent"],"default":"customize","description":"The type of multi-shot video generation"},"generate_audio":{"type":"boolean","default":true,"description":"Whether to generate audio for the video."},"negative_prompt":{"type":"string","description":"The description of elements to avoid in the generated video."},"cfg_scale":{"type":"number","minimum":0,"maximum":1,"description":"The CFG (Classifier Free Guidance) scale is a measure of how close you want the model to stick to your prompt."}},"required":["model"],"title":"klingai/video-v3-pro-text-to-video"}}}},"responses":{"200":{"content":{"application/json":{"schema":{"type":"object","properties":{"id":{"type":"string","description":"The ID of the generated video."},"status":{"type":"string","enum":["queued","generating","completed","error"],"description":"The current status of the generation task."},"video":{"type":"object","nullable":true,"properties":{"url":{"type":"string","format":"uri","description":"The URL where the file can be downloaded from."}},"required":["url"]},"error":{"type":"object","nullable":true,"properties":{"name":{"type":"string"},"message":{"type":"string"}},"required":["name","message"],"description":"Description of the error, if any."},"meta":{"type":"object","nullable":true,"properties":{"usage":{"type":"object","nullable":true,"properties":{"credits_used":{"type":"number","description":"The number of tokens consumed during generation."}},"required":["credits_used"]}},"description":"Additional details about the generation."}},"required":["id","status"]}}}}}}}}} ``` ### Retrieve the generated video from the server After sending a request for video generation, this task is added to the queue. This endpoint lets you check the status of a video generation task using its `id`, obtained from the endpoint described above.\ If the video generation task status is `completed`, the response will include the final result — with the generated video URL and additional metadata. ## GET /v2/video/generations > ```json {"openapi":"3.0.0","info":{"title":"AIML API","version":"1.0.0"},"servers":[{"url":"https://api.aimlapi.com"}],"security":[{"access-token":[]}],"components":{"securitySchemes":{"access-token":{"scheme":"bearer","bearerFormat":"","type":"http","description":"Bearer key","in":"header"}}},"paths":{"/v2/video/generations":{"get":{"operationId":"_v2_video_generations","parameters":[{"name":"generation_id","required":true,"in":"query","schema":{"type":"string"}}],"responses":{"200":{"content":{"application/json":{"schema":{"type":"object","properties":{"id":{"type":"string","description":"The ID of the generated video."},"status":{"type":"string","enum":["queued","generating","completed","error"],"description":"The current status of the generation task."},"video":{"type":"object","nullable":true,"properties":{"url":{"type":"string","format":"uri","description":"The URL where the file can be downloaded from."}},"required":["url"]},"error":{"type":"object","nullable":true,"properties":{"name":{"type":"string"},"message":{"type":"string"}},"required":["name","message"],"description":"Description of the error, if any."},"meta":{"type":"object","nullable":true,"properties":{"usage":{"type":"object","nullable":true,"properties":{"credits_used":{"type":"number","description":"The number of tokens consumed during generation."}},"required":["credits_used"]}},"description":"Additional details about the generation."}},"required":["id","status"]}}}}}}}}} ``` ## Full Example: Generating and Retrieving the Video From the Server The code below creates a video generation task, then automatically polls the server every **15** seconds until it finally receives the video URL. {% tabs %} {% tab title="Python" %} {% code overflow="wrap" %} ```python import requests import time # Insert your AIML API Key instead of : api_key = "" base_url = "https://api.aimlapi.com/v2" # Creating and sending a video generation task to the server def generate_video(): url = f"{base_url}/video/generations" headers = { "Authorization": f"Bearer {api_key}", } data = { "model": "klingai/video-v3-pro-text-to-video", "multi_prompt": [ '''A cheerful white raccoon runs through a giant sequoia forest. Sunlight streams through the tall trees, soft volumetric light, cinematic camera movement following the raccoon from the side, shallow depth of field, animated film style, 3 seconds.''', '''Close-up of the raccoon's face. The raccoon gently raises its head and looks up toward the towering sequoia treetops. Soft light on the fur, expressive eyes, subtle head movement, cinematic close-up, animated film style, 2 seconds.''', '''First-person view from the raccoon's perspective. Massive sequoia trees sway and rustle in the wind high above. Suddenly, a huge pterodactyl flies across the sky, blurred and partially hidden in mist and haze. Slight handheld camera feel, cinematic motion blur, dramatic atmosphere, animated film style, 3 seconds.''' ], "aspect_ratio": "16:9", "duration": 8 } response = requests.post(url, json=data, headers=headers) if response.status_code >= 400: print(f"Error: {response.status_code} - {response.text}") else: response_data = response.json() return response_data # Requesting the result of the task from the server using the generation_id def get_video(gen_id): url = f"{base_url}/video/generations" params = { "generation_id": gen_id, } headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json" } response = requests.get(url, params=params, headers=headers) return response.json() def main(): # Running video generation and getting a task id gen_response = generate_video() gen_id = gen_response.get("id") print("Generation ID: ", gen_id) # Trying to retrieve the video from the server every 15 sec if gen_id: start_time = time.time() timeout = 1000 # 1000 sec = 16 min 40 sec while time.time() - start_time < timeout: response_data = get_video(gen_id) if response_data is None: print("Error: No response from API") break status = response_data.get("status") if status in ["queued", "generating"]: print(f"Status: {status}. Checking again in 15 seconds.") time.sleep(15) else: print("Processing complete:\n", response_data) return response_data print("Timeout reached. Stopping.") return None if __name__ == "__main__": main() ``` {% endcode %} {% endtab %} {% tab title="JavaScript" %} {% code overflow="wrap" %} ```javascript // Insert your AIML API Key instead of const apiKey = ""; const baseUrl = "https://api.aimlapi.com/v2"; const https = require("https"); const { URL } = require("url"); // Creating and sending a video generation task to the server function generateVideo(callback) { const data = JSON.stringify({ model: 'klingai/video-v3-pro-text-to-video', multi_prompt: [ `A cheerful white raccoon runs through a giant sequoia forest. Sunlight streams through the tall trees, soft volumetric light, cinematic camera movement following the raccoon from the side, shallow depth of field, animated film style, 3 seconds.`, `Close-up of the raccoon's face. The raccoon gently raises its head and looks up toward the towering sequoia treetops. Soft light on the fur, expressive eyes, subtle head movement, cinematic close-up, animated film style, 2 seconds.`, `First-person view from the raccoon's perspective. Massive sequoia trees sway and rustle in the wind high above. Suddenly, a huge pterodactyl flies across the sky, blurred and partially hidden in mist and haze. Slight handheld camera feel, cinematic motion blur, dramatic atmosphere, animated film style, 3 seconds.` ], aspect_ratio: '16:9', duration: 8 }); const url = new URL(`${baseUrl}/video/generations`); const options = { method: "POST", headers: { "Authorization": `Bearer ${apiKey}`, "Content-Type": "application/json", "Content-Length": Buffer.byteLength(data) } }; const req = https.request(url, options, (res) => { let body = ""; res.on("data", (chunk) => body += chunk); res.on("end", () => { if (res.statusCode >= 400) { console.error(`Error: ${res.statusCode} - ${body}`); callback(null); } else { const result = JSON.parse(body); callback(result); } }); }); req.on("error", (err) => { console.error("Request error:", err); callback(null); }); req.write(data); req.end(); } // Requesting the result of the task from the server using the generation_id function getVideo(genId, callback) { const url = new URL(`${baseUrl}/video/generations`); url.searchParams.append("generation_id", genId); const options = { method: "GET", headers: { "Authorization": `Bearer ${apiKey}`, "Content-Type": "application/json" } }; const req = https.request(url, options, (res) => { let body = ""; res.on("data", (chunk) => body += chunk); res.on("end", () => { const result = JSON.parse(body); callback(result); }); }); req.on("error", (err) => { console.error("Request error:", err); callback(null); }); req.end(); } // Initiates video generation and checks the status every 15 seconds until completion or timeout function main() { generateVideo((genResponse) => { if (!genResponse || !genResponse.id) { console.error("No generation ID received."); return; } const genId = genResponse.id; console.log("Generation ID:", genId); const timeout = 1000 * 1000; // 1000 sec = 16 min 40 sec const interval = 15 * 1000; // 15 sec const startTime = Date.now(); const checkStatus = () => { if (Date.now() - startTime >= timeout) { console.log("Timeout reached. Stopping."); return; } getVideo(genId, (responseData) => { if (!responseData) { console.error("Error: No response from API"); return; } const status = responseData.status; if (["queued", "generating"].includes(status)) { console.log(`Status: ${status}. Checking again in 15 seconds.`); setTimeout(checkStatus, interval); } else { console.log("Processing complete:\n", responseData); } }); }; checkStatus(); }) } main(); ``` {% endcode %} {% endtab %} {% endtabs %}
Response {% code overflow="wrap" %} ```json5 Generation ID: qAfQfm0KEz8jbBewFuxEp Status: queued. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Status: generating. Checking again in 15 seconds. Processing complete: { id: 'qAfQfm0KEz8jbBewFuxEp', status: 'completed', video: { url: 'https://cdn.aimlapi.com/flamingo/files/b/0a8d6a55/NmxY5hbIUQfYwDRls2yix_output.mp4' } } ``` {% endcode %}
**Processing time**: \~ 6 min 21 sec. **Generated video** (1280x720, with sound): {% embed url="" %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.aimlapi.com/api-references/video-models/kling-ai/v3-pro-text-to-video.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.