minimax/h3/text-to-video

Text-zu-Video

MiniMax H3 Text-to-Video API by MiniMax

minimax/h3/text-to-video

Text-to-video

MiniMax H3 text-to-video: generate a cinematic video from a text prompt. Supports 2K, 5-15s., and 16:9/9:16/1:1/adaptive aspect ratios.

Eingabe

Prompt *

Auflösung

Dauer

Seitenverhältnis

Ausgabe

Inaktiv

Ihre generierten Videos erscheinen hier

Konfigurieren Sie Parameter und klicken Sie auf Ausführen, um mit der Generierung zu beginnen

Jede Ausführung kostet $0.14. Für $10 können Sie ca. 71 Mal ausführen.

Sie können fortfahren mit:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameter

Codebeispiel
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/h3/text-to-video",  # Required. model name
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. The text prompt describing the video to generate
    "resolution": "2K",  # The resolution of the generated video. options: 2K
    "duration": 8,  # The duration of the generated video in seconds
    "ratio": "1:1",  # The aspect ratio of the generated video. options: 21:9 | 16:9 | 4:3 | 1:1 | 3:4 | 9:16
}

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()

Installieren

Installieren Sie das erforderliche Paket für Ihre Programmiersprache.

pip install requests

Authentifizierung

Alle API-Anfragen erfordern eine Authentifizierung über einen API-Schlüssel. Sie können Ihren API-Schlüssel über das Atlas Cloud Dashboard erhalten.

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

HTTP-Header

import os

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

Schützen Sie Ihren API-Schlüssel

Geben Sie Ihren API-Schlüssel niemals in clientseitigem Code oder öffentlichen Repositories preis. Verwenden Sie stattdessen Umgebungsvariablen oder einen Backend-Proxy.

Anfrage senden

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())

Anfrage senden

Senden Sie eine asynchrone Generierungsanfrage. Die API gibt eine Vorhersage-ID zurück, mit der Sie den Status prüfen und das Ergebnis abrufen können.

POST/api/v1/model/generateVideo

Anfragekörper

import requests

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

data = {
    "model": "minimax/h3/text-to-video",
    "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['data']['id']}")
print(f"Status: {result['data']['status']}")

Antwort

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Status prüfen

Fragen Sie den Vorhersage-Endpunkt ab, um den aktuellen Status Ihrer Anfrage zu überprüfen.

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

Abfrage-Beispiel

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)

Statuswerte

processingDie Anfrage wird noch verarbeitet.

completedDie Generierung ist abgeschlossen. Ergebnisse sind verfügbar.

succeededDie Generierung war erfolgreich. Ergebnisse sind verfügbar.

failedDie Generierung ist fehlgeschlagen. Überprüfen Sie das Fehlerfeld.

Abgeschlossene Antwort

{
  "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"
  }
}

Dateien hochladen

Laden Sie Dateien in den Atlas Cloud Speicher hoch und erhalten Sie eine URL, die Sie in Ihren API-Anfragen verwenden können. Verwenden Sie multipart/form-data zum Hochladen.

POST/api/v1/model/uploadMedia

Upload-Beispiel

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}")

Antwort

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

Eingabe-Schema

Die folgenden Parameter werden im Anfragekörper akzeptiert.

Gesamt: 5Erforderlich: 2Optional: 3

modelstringrequired

model name

Default: "minimax/h3/text-to-video"

promptstringrequired

The text prompt describing the video to generate.

resolutionstring

The resolution of the generated video.

Default: "2K"

durationnumber

The duration of the generated video in seconds.

Default: 8

56789101112131415

ratiostring

The aspect ratio of the generated video.

Default: "1:1"

21:916:94:31:13:49:16

Beispiel-Anfragekörper

{
  "model": "minimax/h3/text-to-video",
  "prompt": "A beautiful landscape",
  "resolution": "2K",
  "duration": 8,
  "ratio": "1:1"
}

Ausgabe-Schema

Die API gibt eine Vorhersage-Antwort mit den generierten Ausgabe-URLs zurück.

created_atstring

ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

idstring

Unique identifier for the prediction, the ID of the prediction to get.

modelstring

Model ID used for the prediction.

outputsarray

Array of URLs to the generated content (empty when status is not completed).

statusstring

Status of the task: created, processing, completed, or failed.

Beispielantwort

{
  "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 integriert über 400 KI-Modelle direkt in Ihren KI-Programmierassistenten. Ein Befehl zur Installation, dann generieren Sie per natürlicher Sprache Bilder und Videos und chatten mit LLMs.

Unterstützte Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ unterstützte clients

Installieren

npx skills add AtlasCloudAI/atlas-cloud-skills

API-Schlüssel einrichten

Erhalten Sie Ihren API-Schlüssel über das Atlas Cloud Dashboard und setzen Sie ihn als Umgebungsvariable.

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

Funktionen

Nach der Installation können Sie natürliche Sprache in Ihrem KI-Assistenten verwenden, um auf alle Atlas Cloud Modelle zuzugreifen.

BildgenerierungGenerieren Sie Bilder mit Modellen wie Nano Banana 2, Z-Image und mehr.

VideoerstellungErstellen Sie Videos aus Text oder Bildern mit Kling, Vidu, Veo usw.

LLM-ChatChatten Sie mit Qwen, DeepSeek und anderen großen Sprachmodellen.

Medien-UploadLaden Sie lokale Dateien für Bildbearbeitung und Bild-zu-Video-Workflows hoch.

Mehr erfahren

github.com/AtlasCloudAI/atlas-cloud-skills

MCP-Server

Der Atlas Cloud MCP-Server verbindet Ihre IDE mit über 400 KI-Modellen über das Model Context Protocol. Funktioniert mit jedem MCP-kompatiblen Client.

Unterstützte Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ unterstützte clients

Installieren

npx -y atlascloud-mcp

Konfiguration

Fügen Sie die folgende Konfiguration zur MCP-Einstellungsdatei Ihrer IDE hinzu.

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

Verfügbare Werkzeuge

atlas_generate_imageGenerieren Sie Bilder aus Textbeschreibungen.

atlas_generate_videoErstellen Sie Videos aus Text oder Bildern.

atlas_chatChatten Sie mit großen Sprachmodellen.

atlas_list_modelsDurchsuchen Sie über 400 verfügbare KI-Modelle.

atlas_quick_generateInhaltserstellung in einem Schritt mit automatischer Modellauswahl.

atlas_upload_mediaLaden Sie lokale Dateien für API-Workflows hoch.

Mehr erfahren

github.com/AtlasCloudAI/mcp-server

API-Schema

{
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "minimax/h3/text-to-video"
          },
          "prompt": {
            "description": "The text prompt describing the video to generate.",
            "type": "string"
          },
          "resolution": {
            "default": "2K",
            "description": "The resolution of the generated video.",
            "enum": [
              "2K"
            ],
            "type": "string",
            "x-ui-component": "select"
          },
          "duration": {
            "default": 8,
            "description": "The duration of the generated video in seconds.",
            "enum": [
              5,
              6,
              7,
              8,
              9,
              10,
              11,
              12,
              13,
              14,
              15
            ],
            "type": "number",
            "x-ui-component": "select"
          },
          "ratio": {
            "default": "1:1",
            "description": "The aspect ratio of the generated video.",
            "enum": [
              "21:9",
              "16:9",
              "4:3",
              "1:1",
              "3:4",
              "9:16"
            ],
            "type": "string",
            "x-ui-component": "select"
          }
        },
        "required": [
          "model",
          "prompt"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "resolution",
          "duration",
          "ratio"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "created_at": {
            "description": "ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).",
            "format": "date-time",
            "type": "string"
          },
          "has_nsfw_contents": {
            "description": "Array of boolean values indicating NSFW detection for each output.",
            "items": {
              "type": "boolean"
            },
            "type": "array"
          },
          "id": {
            "description": "Unique identifier for the prediction, the ID of the prediction to get.",
            "type": "string"
          },
          "model": {
            "description": "Model ID used for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the generated content (empty when status is not completed).",
            "items": {
              "type": "string"
            },
            "type": "array"
          },
          "status": {
            "description": "Status of the task: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          }
        },
        "type": "object"
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  },
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "openapi": "3.0.0",
  "paths": {
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    },
    "/api/v1/model/generateVideo": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ]
}

LLM-freundliche Prompt-Vorlage

# minimax/h3/text-to-video

> MiniMax H3 text-to-video: generate a cinematic video from a text prompt. Supports 2K, 5-15s., and 16:9/9:16/1:1/adaptive aspect ratios.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateVideo` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `minimax/h3/text-to-video`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"minimax/h3/text-to-video"`

- **`prompt`** (`string`, _required_):
  The text prompt describing the video to generate.

- **`resolution`** (`string`, _optional_):
  The resolution of the generated video.
  - Default: `"2K"`
  - Options: "2K"

- **`duration`** (`number`, _optional_):
  The duration of the generated video in seconds.
  - Default: `8`
  - Options: 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15

- **`ratio`** (`string`, _optional_):
  The aspect ratio of the generated video.
  - Default: `"1:1"`
  - Options: "21:9", "16:9", "4:3", "1:1", "3:4", "9:16"



**Required Parameters Example**:

```json
{
  "model": "minimax/h3/text-to-video",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "minimax/h3/text-to-video",
  "prompt": "",
  "resolution": "2K",
  "duration": 8,
  "ratio": "1:1"
}
```


### Output Schema

The API returns the following output format:


- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  Array of boolean values indicating NSFW detection for each output.

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction, the ID of the prediction to get.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated content (empty when status is not completed).

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example Response**:

```json
{
  "created_at": "",
  "has_nsfw_contents": [],
  "id": "",
  "model": "",
  "outputs": [
    ""
  ],
  "status": "",
  "urls": {}
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "minimax/h3/text-to-video",
  "prompt": "",
  "resolution": "2K",
  "duration": 8,
  "ratio": "1:1"
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/minimax/h3/text-to-video)

Cinematic medium close-up of a desert warrior wearing a high-tech dust mask and stillsuit, glowing piercing bright blue eyes (eyes of Ibad). The wind blows fine dust across their face. Warm desert sunlight casting sharp side shadows. Cinematic focus, shallow depth of field, slow-motion film grain, photorealistic, Dune style, 8k.

Wird geladen...

MiniMax H3 Text-to-Video

MiniMax H3 Text-to-Video is a state-of-the-art AI video generation model that creates cinematic, high-fidelity videos directly from a text prompt. With crisp detail up to 2K, smooth natural motion, and flexible aspect ratios, it turns a single description into a polished clip.

Why Choose This?

High resolution output Generate videos in 2K quality.
Cinematic motion Fluid camera work and lifelike movement from a plain-text description.
Flexible aspect ratios 16:9, 9:16, 1:1, or adaptive to fit any platform.
Selectable duration Produce 5s or 10s clips.

Parameters

Parameter	Required	Description
prompt	Yes	Text description of the scene, subject, and action
resolution	No	Output quality: 2K (default)
duration	No	Video length in seconds: 8 (default), 5-10
ratio	No	Aspect ratio: adaptive (default), 9:16, 1:1, 4:3

How to Use

Write your prompt — describe the scene, subject, lighting, and motion in detail.
Set resolution — higher for quality, lower for faster generation.
Choose an aspect ratio — match your target platform, or use adaptive.
Adjust duration — pick 5s or 10s.
Run — submit and download your video.

Pricing

Billed per second of generated video, by resolution:

Resolution	Cost per second
2K	$0.14
768p	$0.10

Best Use Cases

Social Media Content — Short-form clips for TikTok, Reels, and Stories.
Concept Visualization — Bring ideas to life without filming.
Marketing Videos — Produce promotional content from text.
Storytelling — Create narrative scenes for creative projects.

Pro Tips

Be specific about camera angle, lighting, mood, and motion.
Use "adaptive" ratio to let the model choose the best framing for the scene.
Higher resolution (2K) suits hero shots; 768P is great for quick iteration.
Describe environmental effects (wind, smoke, golden-hour light) for richer results.

Notes

A prompt is required.
Supported durations are 5s and 10s.
Generation is asynchronous — submit, then poll for the finished video.

MiniMax H3 Image-to-Video — Animate a first-frame image (optionally with a last frame).
MiniMax H3 Reference-to-Video — Keep a subject consistent from a reference image.

MiniMax H3 Image-to-Video

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MiniMax H3 Reference-to-Video

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Eine API für alle Media-KI.

Alle Modelle erkunden

MiniMax H3 Text-to-Video API by MiniMax

Eingabe

Ausgabe

Parameter

Codebeispiel

Installieren

Authentifizierung

HTTP-Header

Anfrage senden

Anfrage senden

Anfragekörper

Antwort

Status prüfen

Abfrage-Beispiel

Statuswerte

Abgeschlossene Antwort

Dateien hochladen

Upload-Beispiel

Antwort

Eingabe-Schema

Beispiel-Anfragekörper

Ausgabe-Schema

Beispielantwort

Atlas Cloud Skills

Unterstützte Clients

Installieren

API-Schlüssel einrichten

Funktionen

MCP-Server

Unterstützte Clients

Installieren

Konfiguration

Verfügbare Werkzeuge

API-Schema

LLM-freundliche Prompt-Vorlage

MiniMax H3 Text-to-Video

Why Choose This?

Parameters

How to Use

Pricing

Best Use Cases

Pro Tips

Notes

Related Models

Ähnliche Modelle Erkunden

MiniMax H3 Image-to-Video

MiniMax H3 Reference-to-Video

Hailuo-2.3 t2v Standard

Hailuo-2.3 t2v Pro

Hailuo-2.3 i2v Standard

Hailuo-2.3 i2v Pro

Hailuo-2.3 Fast

Hailuo-02 t2v Pro

Hailuo-02 Fast

Hailuo 02 Pro

Hailuo 02 t2v Standard

Hailuo 02 i2v Standard

Hailuo 02 i2v Pro

Hailuo 02 Standard

Sync.so Lipsync v3

VEED Lipsync

Eine API für alle Media-KI.

Eingabe

Ausgabe

Parameter

Codebeispiel

Installieren

Authentifizierung

HTTP-Header

Anfrage senden

Anfrage senden

Anfragekörper

Antwort

Status prüfen

Abfrage-Beispiel

Statuswerte

Abgeschlossene Antwort

Dateien hochladen

Upload-Beispiel

Antwort