minimax/hailuo-02/fast

Imagen a Video

Hailuo 02 Fast API by MiniMax

minimax/hailuo-02/fast

Fast

Hailuo 02 is a new AI video generation model from Hailuo AI.

Entrada

Cargando configuración de parámetros...

Salida

Inactivo

Los videos generados se mostrarán aquí

Configura los parámetros y haz clic en ejecutar para comenzar a generar

Cada ejecución costará $0.1. Con $10 puedes ejecutar aproximadamente 100 veces.

Puedes continuar con:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parámetros

Ejemplo de código
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()

Instalar

Instala el paquete necesario para tu lenguaje de programación.

bash

pip install requests

Autenticación

Todas las solicitudes de API requieren autenticación mediante una clave de API. Puedes obtener tu clave de API desde el panel de Atlas Cloud.

bash

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

Encabezados HTTP

python

import os

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

Mantén tu clave de API segura

Nunca expongas tu clave de API en código del lado del cliente ni en repositorios públicos. Usa variables de entorno o un proxy de backend en su lugar.

Enviar una solicitud

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())

Enviar una solicitud

Envía una solicitud de generación asíncrona. La API devuelve un ID de predicción que puedes usar para verificar el estado y obtener el resultado.

POST/api/v1/model/generateVideo

Cuerpo de la solicitud

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']}")

Respuesta

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

Verificar estado

Consulta el endpoint de predicción para verificar el estado actual de tu solicitud.

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

Ejemplo de 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)

Valores de estado

processingLa solicitud aún se está procesando.

completedLa generación está completa. Las salidas están disponibles.

succeededLa generación fue exitosa. Las salidas están disponibles.

failedLa generación falló. Verifica el campo de error.

Respuesta completada

{
  "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"
  }
}

Subir archivos

Sube archivos al almacenamiento de Atlas Cloud y obtén una URL que puedes usar en tus solicitudes de API. Usa multipart/form-data para subir.

POST/api/v1/model/uploadMedia

Ejemplo de carga

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}")

Respuesta

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

Schema de entrada

Los siguientes parámetros se aceptan en el cuerpo de la solicitud.

Total: 0Obligatorio: 0Opcional: 0

No hay parámetros disponibles.

Ejemplo de cuerpo de solicitud

json

{
  "model": "minimax/hailuo-02/fast"
}

Schema de salida

La API devuelve una respuesta de predicción con las URL de salida generadas.

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

Ejemplo de respuesta

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 más de 300 modelos de IA directamente en tu asistente de codificación con IA. Un solo comando para instalar y luego usa lenguaje natural para generar imágenes, videos y chatear con LLM.

Clientes compatibles

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ clientes compatibles

Instalar

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Configurar clave de API

Obtén tu clave de API desde el panel de Atlas Cloud y configúrala como variable de entorno.

bash

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

Funcionalidades

Una vez instalado, puedes usar lenguaje natural en tu asistente de IA para acceder a todos los modelos de Atlas Cloud.

Generación de imágenesGenera imágenes con modelos como Nano Banana 2, Z-Image y más.

Creación de videosCrea videos a partir de texto o imágenes con Kling, Vidu, Veo, etc.

Chat con LLMChatea con Qwen, DeepSeek y otros modelos de lenguaje de gran escala.

Carga de mediosSube archivos locales para flujos de trabajo de edición de imágenes e imagen a video.

Más información

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server conecta tu IDE con más de 300 modelos de IA a través del Model Context Protocol. Funciona con cualquier cliente compatible con MCP.

Clientes compatibles

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ clientes compatibles

Instalar

bash

npx -y atlascloud-mcp

Configuración

Agrega la siguiente configuración al archivo de configuración de MCP de tu IDE.

json

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

Herramientas disponibles

atlas_generate_imageGenera imágenes a partir de indicaciones de texto.

atlas_generate_videoCrea videos a partir de texto o imágenes.

atlas_chatChatea con modelos de lenguaje de gran escala.

atlas_list_modelsExplora más de 300 modelos de IA disponibles.

atlas_quick_generateCreación de contenido en un solo paso con selección automática de modelo.

atlas_upload_mediaSube archivos locales para flujos de trabajo de API.

Más información

github.com/AtlasCloudAI/mcp-server

API Schema

Schema no disponible

Por favor inicia sesión para ver el historial de solicitudes

Necesitas iniciar sesión para acceder al historial de solicitudes del modelo.

Iniciar Sesión

MiniMax 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.