Kling v3.0 Std Image-to-Video API by Kuaishou
kwaivgi/kling-v3.0-std/image-to-video
Image-to-video
Kling v3.0 Standard Image-to-Video model by Kuaishou. High-quality video generation from images.
INPUT
Loading parameter configuration...
OUTPUT
IdleYour generated videos will appear here
Configure your settings and click Run to get started
Your request will cost $0.071 per run. For $10 you can run this model approximately 140 times.
Here's what you can do next:
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-v3.0-std/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 requestsAutenticazione
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/generateVideoCorpo 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-v3.0-std/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/uploadMediaEsempio 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-v3.0-std/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.
completed_atstring
ISO 8601 timestamp when the prediction was completed.
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-skillsConfigura 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
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-mcpConfigurazione
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
API Schema
Schema not availableNo examples available
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Log InKling V3.0 Standard Image-to-Video
Kling V3.0 Standard Image-to-Video is Kuaishou's latest image-to-video generation model. Upload a reference image and describe the motion — the model generates cinematic video with optional synchronized sound, voice support, and start-to-end frame guidance.
Why Choose This?
- Latest Kling generation V3.0 delivers improved motion quality and visual fidelity over V2.6.
- Start-end frame guidance Optional end image for controlled transitions between two frames.
- Sound generation Optional synchronized sound effects generated alongside the video.
- Voice list support Add up to 2 custom voice entries for character dialogue.
- CFG scale control Fine-tune the balance between prompt adherence and creative freedom.
Parameters
| Parameter | Required | Description |
|---|---|---|
| prompt | No | Text description of the desired motion and action |
| negative_prompt | No | Elements to exclude from generation |
| image | Yes | Start frame image to animate (URL or upload) |
| end_image | No | End frame image for guided transitions |
| duration | No | Video length: 5 or 10 seconds (default: 5) |
| cfg_scale | No | Prompt adherence strength (default: 0.5) |
| sound | No | Generate synchronized sound (default: disabled) |
| voice_list | No | Custom voice entries, up to 2 (click "+ Add Item") |
How to Use
- Upload your image — provide the reference image to animate.
- Write your prompt (optional) — describe the motion, camera movement, and action.
- Upload end image (optional) — provide an end frame for guided transitions.
- Add negative prompt (optional) — specify what you want to avoid.
- Set duration — 5 seconds or 10 seconds.
- Adjust cfg_scale (optional) — higher for stricter prompt following, lower for more freedom.
- Enable sound (optional) — generate synchronized audio with the video.
- Add voices (optional) — add up to 2 voice entries for dialogue.
- Run — submit and download your video.
Best Use Cases
- Photo Animation — Bring portraits, landscapes, and product images to life.
- Scene Transitions — Use start and end frames for smooth visual transitions.
- Social Media Content — Create engaging videos with sound from still images.
- Marketing & Ads — Generate dynamic promotional videos from product photos.
- Storytelling — Animate scenes with synchronized audio and dialogue.
Pro Tips
- Use clear, descriptive prompts with specific motion details for best results.
- Add an end_image for controlled transitions between two visual states.
- Enable sound for a complete video experience with synchronized audio.
- Use negative prompts to avoid artifacts (e.g., "blurry, low quality, distorted").
- Lower cfg_scale for more creative variation, higher for strict prompt adherence.
- Use high-quality source images for better video results.
Notes
- Image is the only required field; prompt is optional but recommended.
- Duration options are 5 or 10 seconds only.
- Voice list supports a maximum of 2 entries.
- Ensure uploaded image URLs are publicly accessible.
Related Models
- Kling V3.0 Standard Text-to-Video — Generate video from text descriptions with V3.0 quality.
- Kling V2.6 Standard Image-to-Video — Previous generation image-to-video.
- Kling V2.6 Standard Text-to-Video — Previous generation text-to-video.
