minimax/h3/image-to-video

image-to-video

MiniMax H3 Image-to-Video API by MiniMax

minimax/h3/image-to-video

Image-to-video

MiniMax H3 image-to-video: animate a first-frame image (optionally with a last frame) driven by a text prompt. Supports 2K, 5-15s.

Compare models

MiniMax H3 Image-to-Video is developed by MiniMax. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.

INPUT

Prompt *

Image *

You can drag and drop a file or click to upload

MAX:1

End Image

You can drag and drop a file or click to upload

MAX:1

Resolution *

Duration *

Aspect Ratio

OUTPUT

Idle

Your generated videos will appear here

Configure your settings and click Run to get started

Your request will cost $0.1 per run. For $10 you can run this model approximately 100 times.

Here's what you can do next:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parameters

Code Example
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/image-to-video",  # Required. model name
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. The text prompt describing the motion / action for the video
    "image": "example_value",  # Required. First frame of the video
    "end_image": "example_value",  # Optional last frame of the video
    "resolution": "2K",  # Required. The resolution of the generated video. options: 768P | 2K
    "duration": 8,  # Required. The duration of the generated video in seconds
    "ratio": "adaptive",  # The aspect ratio of the generated video. options: adaptive
}

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()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

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

HTTP Headers

import os

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

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

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())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateVideo

Request Body

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/image-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']}")

Response

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

Check Status

Poll the prediction endpoint to check the current status of your request.

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

Polling Example

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)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "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"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

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}")

Response

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

Input Schema

The following parameters are accepted in the request body.

Total: 7Required: 5Optional: 2

modelstringrequired

model name

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

promptstringrequired

The text prompt describing the motion / action for the video.

imagestringrequired

First frame of the video. Public URL or Base64. Formats: png, jpeg, jpg, webp.

end_imagestring

Optional last frame of the video. Public URL or Base64. Formats: png, jpeg, jpg, webp.

resolutionstringrequired

The resolution of the generated video.

Default: "2K"

768P2K

durationnumberrequired

The duration of the generated video in seconds.

Default: 8

456789101112131415

ratiostring

The aspect ratio of the generated video. Use 'adaptive' to let the model choose.

Default: "adaptive"

adaptive

Example Request Body

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

Output Schema

The API returns a prediction response with the generated output URLs.

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.

Example Response

{
  "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 integrates 400+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

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

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 400+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

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

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 400+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "minimax/h3/image-to-video"
          },
          "prompt": {
            "description": "The text prompt describing the motion / action for the video.",
            "type": "string"
          },
          "image": {
            "description": "First frame of the video. Public URL or Base64. Formats: png, jpeg, jpg, webp.",
            "type": "string",
            "x-ui-component": "uploaders"
          },
          "end_image": {
            "description": "Optional last frame of the video. Public URL or Base64. Formats: png, jpeg, jpg, webp.",
            "type": "string",
            "x-ui-component": "uploaders"
          },
          "resolution": {
            "default": "2K",
            "description": "The resolution of the generated video.",
            "enum": [
              "768P",
              "2K"
            ],
            "type": "string",
            "x-ui-component": "select"
          },
          "duration": {
            "default": 8,
            "description": "The duration of the generated video in seconds.",
            "enum": [
              4,
              5,
              6,
              7,
              8,
              9,
              10,
              11,
              12,
              13,
              14,
              15
            ],
            "type": "number",
            "x-ui-component": "select"
          },
          "ratio": {
            "default": "adaptive",
            "description": "The aspect ratio of the generated video. Use 'adaptive' to let the model choose.",
            "enum": [
              "adaptive"
            ],
            "type": "string",
            "x-ui-component": "select"
          }
        },
        "required": [
          "model",
          "prompt",
          "image",
          "resolution",
          "duration"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "image",
          "end_image",
          "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-Friendly Prompt Template

# minimax/h3/image-to-video

> MiniMax H3 image-to-video: animate a first-frame image (optionally with a last frame) driven by a text prompt. Supports 2K, 5-15s.


## 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/image-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/image-to-video"`

- **`prompt`** (`string`, _required_):
  The text prompt describing the motion / action for the video.

- **`image`** (`string`, _required_):
  First frame of the video. Public URL or Base64. Formats: png, jpeg, jpg, webp.

- **`end_image`** (`string`, _optional_):
  Optional last frame of the video. Public URL or Base64. Formats: png, jpeg, jpg, webp.

- **`resolution`** (`string`, _required_):
  The resolution of the generated video.
  - Default: `"2K"`
  - Options: "768P", "2K"

- **`duration`** (`number`, _required_):
  The duration of the generated video in seconds.
  - Default: `8`
  - Options: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15

- **`ratio`** (`string`, _optional_):
  The aspect ratio of the generated video. Use 'adaptive' to let the model choose.
  - Default: `"adaptive"`
  - Options: "adaptive"



**Required Parameters Example**:

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


**Full Example**:

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


### 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/image-to-video",
  "prompt": "",
  "image": "",
  "end_image": "",
  "resolution": "2K",
  "duration": 8,
  "ratio": "adaptive"
}'

# 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/image-to-video)

The warrior raised the sword in his hand high and brandished it.

MiniMax H3 Image-to-Video

MiniMax H3 Image-to-Video brings a static image to life. Provide a first-frame image — and, optionally, a last frame — plus a prompt describing the motion, and the model generates a smooth, cinematic clip at up to 2K that starts (and optionally ends) exactly on your images.

Why Choose This?

Image-driven generation Animate any image with natural, controllable motion.
First & last frame control Set the opening frame, and optionally pin the closing frame for a precise transition.
High resolution output Generate videos in 2K quality.
Flexible aspect ratios Automatically determined by the input image (always adaptive).
Selectable duration Produce 4–15s clips.

Parameters

Parameter	Required	Description
prompt	Yes	Text description of the desired motion and action
image	Yes	First frame of the video (public URL or Base64)
end_image	No	Last frame of the video (public URL or Base64)
resolution	Yes	Video resolution. Available options: `768P`, `2K`
duration	Yes	Duration of the generated video in seconds. Integer between 4 and 15. Available options: 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
ratio	No	Aspect ratio of the generated video. For image-to-video, the aspect ratio is determined by the input image and is always `adaptive`; passing any other value is ignored.

How to Use

Upload your first-frame image — the video will start from this image.
(Optional) Upload a last-frame image — the video will end on this image.
Write your prompt — describe the motion, camera movement, and action.
Set resolution and duration — balance quality against generation speed.
Choose an aspect ratio — match your target platform, or use adaptive.
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

Photo Animation — Bring portraits, landscapes, and product images to life.
Start/End Transitions — Morph smoothly from one image to another.
Marketing & Ads — Turn product photos into dynamic promotional videos.
Storytelling — Animate illustrations and artwork for narratives.

Pro Tips

Be specific about movement direction, speed, and camera angles.
When using a last frame, keep the two images' aspect ratios close for a clean transition.
Use high-quality source images for better video results.
Describe environmental effects (wind, smoke, dust) for more immersive motion.

Notes

Both prompt and first-frame image are required.
The last-frame image is optional; when provided, the clip ends on it.
Ensure image URLs are publicly accessible.
Generation is asynchronous — submit, then poll for the finished video.

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MiniMax H3 Reference-to-Video — Keep a subject consistent from a reference image.

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MiniMax H3 Image-to-Video API by MiniMax

INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

Response

Input Schema

Example Request Body

Output Schema

Example Response

Atlas Cloud Skills

Supported Clients

Install

Setup API Key

Capabilities

MCP Server

Supported Clients

Install

Configuration

Available Tools

API Schema

LLM-Friendly Prompt Template

MiniMax H3 Image-to-Video

Why Choose This?

Parameters

How to Use

Pricing

Best Use Cases

Pro Tips

Notes

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Sync.so Lipsync v3

VEED Lipsync

One API for All Media AI.

INPUT

OUTPUT

Parameters

Code Example

Install

Authentication

HTTP Headers

Submit a request

Submit a Request

Request Body

Response

Check Status

Polling Example

Status Values

Completed Response

Upload Files

Upload Example

Response