kwaivgi/kling-v2.5-turbo-pro/image-to-video

image-to-video

TURBOPRO

Kling v2.5 Turbo Pro Image-to-Video API by Kuaishou

kwaivgi/kling-v2.5-turbo-pro/image-to-video

Image-to-video

Transforms stills into lifelike video clips at 2× faster speed while preserving fine texture and lighting consistency.

INPUT

Loading parameter configuration...

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.06 per run. For $10 you can run this model approximately 166 times.

Here's what you can do next:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parametri

Esempio di codice
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-v2.5-turbo-pro/image-to-video",
    "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()

Installa

Installa il pacchetto richiesto per il tuo linguaggio.

bash

pip install requests

Autenticazione

Tutte le richieste API richiedono l'autenticazione tramite una chiave API. Puoi ottenere la tua chiave API dalla dashboard di Atlas Cloud.

bash

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

Header HTTP

python

import os

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

Proteggi la tua chiave API

Non esporre mai la tua chiave API nel codice lato client o nei repository pubblici. Utilizza invece variabili d'ambiente o un proxy backend.

Invia una richiesta

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())

Invia una richiesta

Invia una richiesta di generazione asincrona. L'API restituisce un ID di previsione che puoi usare per controllare lo stato e recuperare il risultato.

POST/api/v1/model/generateVideo

Corpo della richiesta

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-v2.5-turbo-pro/image-to-video",
    "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']}")

Risposta

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

Controlla lo stato

Interroga l'endpoint di previsione per verificare lo stato attuale della tua richiesta.

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

Esempio di polling

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)

Valori di stato

processingLa richiesta è ancora in fase di elaborazione.

completedLa generazione è completata. I risultati sono disponibili.

succeededLa generazione è riuscita. I risultati sono disponibili.

failedLa generazione è fallita. Controlla il campo errore.

Risposta completata

{
  "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"
  }
}

Carica file

Carica file nello storage Atlas Cloud e ottieni un URL utilizzabile nelle tue richieste API. Usa multipart/form-data per il caricamento.

POST/api/v1/model/uploadMedia

Esempio di caricamento

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}")

Risposta

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

Schema di input

I seguenti parametri sono accettati nel corpo della richiesta.

Totale: 0Obbligatorio: 0Opzionale: 0

Nessun parametro disponibile.

Esempio di corpo della richiesta

json

{
  "model": "kwaivgi/kling-v2.5-turbo-pro/image-to-video"
}

Schema di output

L'API restituisce una risposta di previsione con gli URL degli output generati.

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

Esempio di risposta

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 integra oltre 300 modelli di IA direttamente nel tuo assistente di codifica IA. Un comando per installare, poi usa il linguaggio naturale per generare immagini, video e chattare con LLM.

Client supportati

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ client supportati

Installa

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Configura chiave API

Ottieni la tua chiave API dalla dashboard di Atlas Cloud e impostala come variabile d'ambiente.

bash

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

Funzionalità

Una volta installato, puoi usare il linguaggio naturale nel tuo assistente IA per accedere a tutti i modelli Atlas Cloud.

Generazione di immaginiGenera immagini con modelli come Nano Banana 2, Z-Image e altri.

Creazione di videoCrea video da testo o immagini con Kling, Vidu, Veo, ecc.

Chat LLMChatta con Qwen, DeepSeek e altri grandi modelli linguistici.

Caricamento mediaCarica file locali per la modifica di immagini e flussi di lavoro da immagine a video.

Scopri di più

github.com/AtlasCloudAI/atlas-cloud-skills

Server MCP

Il server MCP di Atlas Cloud collega il tuo IDE con oltre 300 modelli di IA tramite il Model Context Protocol. Funziona con qualsiasi client compatibile MCP.

Client supportati

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ client supportati

Installa

bash

npx -y atlascloud-mcp

Configurazione

Aggiungi la seguente configurazione al file delle impostazioni MCP del tuo IDE.

json

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

Strumenti disponibili

atlas_generate_imageGenera immagini da prompt testuali.

atlas_generate_videoCrea video da testo o immagini.

atlas_chatChatta con grandi modelli linguistici.

atlas_list_modelsEsplora oltre 300 modelli di IA disponibili.

atlas_quick_generateCreazione di contenuti in un solo passaggio con selezione automatica del modello.

atlas_upload_mediaCarica file locali per i flussi di lavoro API.

Scopri di più

github.com/AtlasCloudAI/mcp-server

API Schema

Schema not available

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Kling 2.5 Turbo Pro (Image-to-Video)

Kling 2.5 Turbo Pro turns a single image and a text prompt into cinematic video with fluid motion and accurate intent. A new text-timing engine, improved dynamics, and faster inference enable high-speed action and complex camera moves with stable frames, while refined conditioning preserves palette, lighting, and mood.

This version additionally supports first–last frame control: you can specify both a starting image and an ending image, and the model will animate a smooth transformation between them.

What makes it stand out?

Better prompt understanding Precisely parses multi-step, causal instructions and turns a single image and prompt into coherent, well-paced shots that stay true to your creative idea.
More realistic look and greater stability Improved dynamics and balanced training data closely mimic real-world motion, even at high speeds and with complex camera moves. Playback is smooth with fewer jitters, tears, and dropped details.
Detail and style consistency Refined image conditioning maintains color, lighting, brushwork, and mood, keeping frames visually unified even during aggressive motion or transitions.
First–last frame animation When you provide both an initial image and a last_image, Kling 2.5 Turbo Pro treats them as keyframes and generates a video that naturally evolves from the first to the last frame.

Inputs

image (required) The starting frame of your video. Composition, style, and subject are primarily taken from this image.
last_image (optional) An optional target frame. If provided, the model interpolates between image and last_image, creating a smooth visual evolution from start to end.
prompt (required) Text description of the scene, actions, camera movement, and style.
negative_prompt (optional) Things you want the model to avoid (for example, blur, text overlays, distortions).
guidance_scale Controls how strongly the model follows the prompt versus being more free-form.
- Lower values = more creative variation.
- Higher values = stricter adherence to the prompt.
duration Length of the generated video:
- 5 seconds
- 10 seconds

Output

A single video clip of the chosen duration, animated from the initial image (and optionally toward the last_image) according to your prompt.

Designed For

Marketing and brand teams – Consistent, on-brand motion spots, feature demos, and campaign assets.
Creators / YouTubers / Shorts teams – Strong narrative motion that boosts watch-through and engagement.
Film / animation studios – Previz, style tests, and technique exploration with reliable dynamics.
Education and training – Turn static diagrams or slides into clear, animated explainers.

How to Use

Upload or paste the URL of your image as the starting frame.
(Optional) Upload a last_image if you want the video to end on a specific frame or design.
Write your prompt, specifying subject, scene, motion, and style.
(Optional) Add a negative_prompt to filter out unwanted artifacts or styles.
Adjust guidance_scale to balance between strict prompt following and looser creativity.
Choose the duration (5 s or 10 s).
Run the model, preview the result, then iterate by tweaking the prompt, images, or guidance_scale until you reach the desired look.