Hailuo 02 is a new AI video generation model from Hailuo AI.
INPUT
OUTPUT
IdleYour request will cost 0.1 per run. For $10 you can run this model approximately 100 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": "minimax/hailuo-02/fast",
"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.
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.
export ATLASCLOUD_API_KEY="your-api-key-here"Header HTTP
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}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.
/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": "minimax/hailuo-02/fast",
"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.
/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.
/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.
Nessun parametro disponibile.
Esempio di corpo della richiesta
{
"model": "minimax/hailuo-02/fast"
}Schema di output
L'API restituisce una risposta di previsione con gli URL degli output generati.
Esempio di risposta
{
"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
Installa
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.
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.
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
Installa
npx -y atlascloud-mcpConfigurazione
Aggiungi la seguente configurazione al file delle impostazioni MCP del tuo IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Strumenti disponibili
API Schema
Schema not availablePlease log in to view request history
You need to be logged in to access your model request history.
Log InMiniMax Hailuo 02 Fast
This is a fast version of Hailuo 02 that can generate videos in 6s and 10s at 512p resolution.
Hailuo 02 is a new AI video generation model, from Hailuo AI, created on MiniMax's evolving framework. It has been fine-tuned to deliver ultra-clear 1080P resolution and unprecedented responsiveness while even handling, the craziest of physics driven scenes.
Indeed, artists have discovered that for highly intricate scenarios, such as gymnastics, MiniMax Hailuo 02 is currently the only model globally capable of delivering such performance. We eagerly invite the community to explore and unlock even more creative possibilities.
Our journey began late last August when we informally launched a demo webpage showcasing an early version of our video generation model. To our surprise, it attracted significant attention and acclaim from talented creators worldwide. This pivotal moment led to the development of Hailuo Video 01, our AI native video generation product, which has since empowered creators to generate over 370 million videos globally.
Returning to our foundational principle of "Intelligence with Everyone," our ambition is to equip global creators to fully unleash their imagination, elevate the quality of their video content, and lower the barriers to video creation. Crucially, we strive to achieve this without imposing prohibitive costs that would limit the widespread accessibility of this technology.
To this end, our team embarked on a quest to develop a more efficient video generation model architecture. This pursuit culminated in the core framework of MiniMax Hailuo 02, which we've named Noise-aware Compute Redistribution (NCR). In essence, the new architecture's central idea is as follows:
At a comparable parameter scale, the new architecture boosts our training and inference efficiency by 2.5 times. This significant gain enables us to implement a much larger parameter model—thereby enhancing its expressive capabilities—without increasing costs for creators. This approach also leaves ample room for inference optimization. We ultimately expanded the model's total parameter count to 3 times that of its predecessor.
A larger parameter count and heightened training efficiency mean our model can learn from a more extensive dataset. The wealth of feedback from Hailuo 01 provided invaluable guidance for our model training strategy. As a result, we expanded our training data volume by 4 times, achieving significant improvements in data quality and diversity.
With this architectural innovation, combined with a threefold increase in parameters and four times the training data, our model has taken a significant leap forward, particularly in its adherence to complex instructions and its rendering of extreme physics. The new model accurately interprets and executes highly detailed prompts, delivering more precise outputs. Furthermore, the efficiency gains from the new architecture also mean we can offer native 1080p video generation at a very affordable price point.
An early iteration of this model was tested by users on the Artificial Analysis Video Arena, where it secured the second position globally. Stay tuned for an upcoming new version!
These model enhancements are now fully integrated into the Hailuo Video web platform, mobile application, and our API platform. We currently offer three distinct versions: 768p-6s, 768p-10s, and 1080p-6s. True to our commitment, and thanks to the aforementioned architectural innovation, we continue to offer creators and developers the most open access and affordable pricing in the industry.
Through sustained technological research and development, coupled with deep collaborations with creators, developers, and artists, our mission and strategic direction have become ever clearer.
MiniMax Hailuo 02 represents a new milestone, and we are poised for rapid advancements in the following areas:
-
Enhancing generation speed
-
Improving alignment, leading to higher generation success rates and improved stability
-
Advancing model features beyond Text-to-Video (T2V) and Image-to-Video (I2V)
And, as always, we remain steadfast in our commitment to relentlessly exploring the upper limits of what technology and art can achieve together.
