kwaivgi/kling-v2.6-pro/motion-control

Beeld-naar-Video

PRO

Kling 2.6 Pro Motion Control turns reference motion clips (dance, action, gesture) into smooth, realistic animations. Upload a character image (or source video) and a motion video; the model transfers the movement while preserving identity and temporal consistency.

Invoer

Parameterconfiguratie laden...

Uitvoer

Inactief

Uw gegenereerde video's verschijnen hier

Configureer parameters en klik op Uitvoeren om te beginnen met genereren

Elke uitvoering kost 0.095. Voor $10 kunt u ongeveer 105 keer uitvoeren.

U kunt doorgaan met:

Video-upscaling Video-uitbreiding

Parameters

Codevoorbeeld
import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "kwaivgi/kling-v2.6-pro/motion-control",
    "prompt": "A beautiful sunset over the ocean with gentle waves",
    "width": 512,
    "height": 512,
    "duration": 3,
    "fps": 24,
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated video:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

video_url = check_status()

Installeren

Installeer het vereiste pakket voor uw programmeertaal.

bash

pip install requests

Authenticatie

Alle API-verzoeken vereisen authenticatie via een API-sleutel. U kunt uw API-sleutel ophalen via het Atlas Cloud dashboard.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP-headers

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Bescherm uw API-sleutel

Stel uw API-sleutel nooit bloot in client-side code of openbare repositories. Gebruik in plaats daarvan omgevingsvariabelen of een backend-proxy.

Een verzoek indienen

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Een verzoek indienen

Dien een asynchroon generatieverzoek in. De API retourneert een voorspellings-ID waarmee u de status kunt controleren en het resultaat kunt ophalen.

POST/api/v1/model/generateVideo

Verzoekinhoud

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "kwaivgi/kling-v2.6-pro/motion-control",
    "input": {
        "prompt": "A beautiful sunset over the ocean with gentle waves"
    }
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")

Antwoord

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Status controleren

Bevraag het voorspellings-eindpunt om de huidige status van uw verzoek te controleren.

GET/api/v1/model/prediction/{prediction_id}

Polling-voorbeeld

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Statuswaarden

processingHet verzoek wordt nog verwerkt.

completedDe generatie is voltooid. Resultaten zijn beschikbaar.

succeededDe generatie is geslaagd. Resultaten zijn beschikbaar.

failedDe generatie is mislukt. Controleer het foutveld.

Voltooid antwoord

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Bestanden uploaden

Upload bestanden naar Atlas Cloud opslag en ontvang een URL die u kunt gebruiken in uw API-verzoeken. Gebruik multipart/form-data om te uploaden.

POST/api/v1/model/uploadMedia

Upload-voorbeeld

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Antwoord

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Invoer-Schema

De volgende parameters worden geaccepteerd in de verzoekinhoud.

Totaal: 0Vereist: 0Optioneel: 0

Geen parameters beschikbaar.

Voorbeeld verzoekinhoud

json

{
  "model": "kwaivgi/kling-v2.6-pro/motion-control"
}

Uitvoer-Schema

De API retourneert een voorspellingsantwoord met de gegenereerde uitvoer-URL's.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for video generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

Voorbeeldantwoord

json

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integreert meer dan 300 AI-modellen rechtstreeks in uw AI-codeerassistent. Eén commando om te installeren, gebruik daarna natuurlijke taal om afbeeldingen, video's te genereren en te chatten met LLMs.

Ondersteunde clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ ondersteunde clients

Installeren

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

API-sleutel instellen

Haal uw API-sleutel op via het Atlas Cloud dashboard en stel deze in als omgevingsvariabele.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

Mogelijkheden

Eenmaal geïnstalleerd kunt u natuurlijke taal gebruiken in uw AI-assistent om toegang te krijgen tot alle Atlas Cloud modellen.

BeeldgeneratieGenereer afbeeldingen met modellen zoals Nano Banana 2, Z-Image en meer.

VideocreatieMaak video's van tekst of afbeeldingen met Kling, Vidu, Veo, enz.

LLM-chatChat met Qwen, DeepSeek en andere grote taalmodellen.

Media uploadenUpload lokale bestanden voor beeldbewerking en afbeelding-naar-video workflows.

Meer informatie

github.com/AtlasCloudAI/atlas-cloud-skills

MCP-server

De Atlas Cloud MCP-server verbindt uw IDE met meer dan 300 AI-modellen via het Model Context Protocol. Werkt met elke MCP-compatibele client.

Ondersteunde clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ ondersteunde clients

Installeren

bash

npx -y atlascloud-mcp

Configuratie

Voeg de volgende configuratie toe aan het MCP-instellingenbestand van uw IDE.

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Beschikbare tools

atlas_generate_imageGenereer afbeeldingen op basis van tekstprompts.

atlas_generate_videoMaak video's van tekst of afbeeldingen.

atlas_chatChat met grote taalmodellen.

atlas_list_modelsBlader door meer dan 300 beschikbare AI-modellen.

atlas_quick_generateContentcreatie in één stap met automatische modelselectie.

atlas_upload_mediaUpload lokale bestanden voor API-workflows.

Meer informatie

github.com/AtlasCloudAI/mcp-server

API Schema

Schema niet beschikbaar

Inloggen om aanvraaggeschiedenis te bekijken

U moet ingelogd zijn om toegang te krijgen tot uw modelaanvraaggeschiedenis.

Inloggen

Kling v2.6 Pro Motion Control

Kling v2.6 Pro Motion Control is Kuaishou's advanced motion transfer model that animates a reference image by applying the movement from a reference video. Upload a character image and a motion clip (like a dance or action sequence), and the model extracts the motion path to generate smooth, realistic video where your subject performs those exact movements.

Key capabilities

Motion extraction and transfer Upload a 3 to 30-second reference video showing any movement (dance, walk cycle, martial arts, gestures), and the model captures the full motion sequence frame-by-frame to apply it to your image.
Full-body motion accuracy The system captures detailed movements including posture, limb positions, and complex actions, ensuring smooth and natural-looking animation even for fast or intricate sequences.
Flexible character orientation control Choose whether the final video follows the reference image's aspect ratio and composition ("image" mode) or the reference video's framing ("video" mode), with duration limits adjusted accordingly.
Audio preservation option Retain the original audio from your reference video or generate silent output, giving you control over the final soundscape.
Prompt-guided refinement Use text prompts to adjust scene details, styling, lighting, and atmosphere while maintaining the core motion transfer from the reference video.

Parameters and how to use

image: (required) The reference image showing the subject you want to animate
video: (required) The reference video containing the motion sequence to transfer
character_orientation: (required) Controls output framing and duration limits
prompt: Text description to refine scene details, style, and atmosphere
keep_original_sound: Whether to preserve audio from the reference video
negative_prompt: Elements to avoid in the generated video

How to use

Prompt

Describe the scene setting, visual style, lighting, and atmosphere you want while the motion is being transferred. The model will apply your reference video's movement to your reference image, so focus your prompt on environmental details rather than the action itself.

Example: "cinematic lighting, shallow depth of field, urban street background, golden hour, film grain"

Media requirements

Images

Max file size: 10 MB
Tip: Use clear, well-lit images showing the full subject for best motion transfer results

Videos

Duration limits depend on character_orientation setting (see below)

Other parameters

character_orientation – (required) Choose one:

image – Output matches the reference image's framing and composition. video – Output matches the reference video's framing and composition. Reference video can be up to 30 seconds.

keep_original_sound – Boolean, defaults to true

true – Preserve audio from the reference video false – Generate silent video output

negative_prompt – Optional text to specify unwanted elements like "blurry, distorted, watermark, low quality, flickering". Max 2,500 characters.

After you finish configuring the parameters, click Run, preview the result, and iterate if needed.

Pricing

Duration (s)	Billed Duration (s)	Total Price (USD)
5	5	$0.560
10	10	$1.120
15	15	$1.680
30	30	$3.360

Notes

Best practices:

For complex movements like dance or martial arts, use reference videos between 3 and 10 seconds showing clear, unobstructed motion
Ensure your reference image shows the subject in good lighting with minimal occlusion
Start with the default settings and use prompts primarily for scene styling rather than motion instructions
The model works best when the reference image subject and reference video subject are similar in type (e.g., both human characters)

Use cases:

Animate character illustrations with real dance choreography or action sequences
Create product demonstration videos by transferring human gestures to animated mascots
Generate character performance clips for storyboarding and concept work
Produce social media content by applying trending motion clips to custom characters

Kling v2.6 Pro Image-to-Video – Generate videos from a single image with prompt-driven motion and optional native audio.
Kling v2.6 Pro Text-to-Video – Create videos entirely from text prompts with cinematic visuals and audio–video co-generation.
Kling Omni Video O1 Reference-to-Video – Maintain subject identity across frames using multi-reference inputs for character-consistent video generation.