minimax/h3/image-to-video

bild-till-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.

Inmatning

Prompt *

Bild *

Du kan dra och släppa filer här eller klicka för att ladda upp

MAX:1

Slutbild

Du kan dra och släppa filer här eller klicka för att ladda upp

MAX:1

Upplösning

Varaktighet

Bildförhållande

Utmatning

Vilande

Dina genererade videor visas här

Konfigurera parametrar och klicka på Kör för att börja generera

Varje körning kostar $0.14. För $10 kan du köra cirka 71 gånger.

Du kan fortsätta med:

Seedance 2.0 Kling v3 Vidu Wan2.7

Parametrar

Kodexempel
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",  # The resolution of the generated video. options: 2K
    "duration": 8,  # The duration of the generated video in seconds
    "ratio": "adaptive",  # The aspect ratio of the generated video
}

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()

Installera

Installera det nödvändiga paketet för ditt programmeringsspråk.

pip install requests

Autentisering

Alla API-förfrågningar kräver autentisering via en API key. Du kan hämta din API key från Atlas Cloud-instrumentpanelen.

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

HTTP-huvuden

import os

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

Håll din API key säker

Exponera aldrig din API key i klientkod eller publika arkiv. Använd miljövariabler eller en backend-proxy istället.

Skicka en förfrågan

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())

Skicka en förfrågan

Skicka en asynkron genereringsförfrågan. API:et returnerar ett prediction ID som du kan använda för att kontrollera statusen och hämta resultatet.

POST/api/v1/model/generateVideo

Förfrågningsinnehåll

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']}")

Svar

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

Kontrollera status

Polla prediction-endpointen för att kontrollera den aktuella statusen för din förfrågan.

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

Polling-exempel

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)

Statusvärden

processingFörfrågan bearbetas fortfarande.

completedGenereringen är klar. Utdata är tillgängliga.

succeededGenereringen lyckades. Utdata är tillgängliga.

failedGenereringen misslyckades. Kontrollera error-fältet.

Slutfört svar

{
  "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"
  }
}

Ladda upp filer

Ladda upp filer till Atlas Cloud-lagring och få en URL som du kan använda i dina API-förfrågningar. Använd multipart/form-data för uppladdning.

POST/api/v1/model/uploadMedia

Uppladdningsexempel

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}")

Svar

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

Input Schema

Följande parametrar accepteras i förfrågningsinnehållet.

Totalt: 7Obligatorisk: 3Valfri: 4

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.

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. Use 'adaptive' to let the model choose.

Default: "adaptive"

adaptive21:916:94:31:13:49:16

Exempel på förfrågningsinnehåll

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

Output Schema

API:et returnerar ett prediction-svar med de genererade utdata-URL:erna.

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.

Exempelsvar

{
  "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 integrerar 400+ AI-modeller direkt i din AI-kodassistent. Ett kommando för att installera, sedan använd naturligt språk för att generera bilder, videor och chatta med LLM.

Stödda klienter

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ stödda klienter

Installera

npx skills add AtlasCloudAI/atlas-cloud-skills

Konfigurera API Key

Hämta din API key från Atlas Cloud-instrumentpanelen och ställ in den som en miljövariabel.

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

Funktioner

När det är installerat kan du använda naturligt språk i din AI-assistent för att komma åt alla Atlas Cloud-modeller.

BildgenereringGenerera bilder med modeller som Nano Banana 2, Z-Image och fler.

VideoskapandeSkapa videor från text eller bilder med Kling, Vidu, Veo m.fl.

LLM-chattChatta med Qwen, DeepSeek och andra stora språkmodeller.

MediauppladdningLadda upp lokala filer för bildredigering och bild-till-video-arbetsflöden.

Läs mer

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server ansluter din IDE med 400+ AI-modeller via Model Context Protocol. Fungerar med alla MCP-kompatibla klienter.

Stödda klienter

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ stödda klienter

Installera

npx -y atlascloud-mcp

Konfiguration

Lägg till följande konfiguration i din IDE:s MCP-inställningsfil.

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

Tillgängliga verktyg

atlas_generate_imageGenerera bilder från textpromptar.

atlas_generate_videoSkapa videor från text eller bilder.

atlas_chatChatta med stora språkmodeller.

atlas_list_modelsBläddra bland 400+ tillgängliga AI-modeller.

atlas_quick_generateInnehållsskapande i ett steg med automatiskt modellval.

atlas_upload_mediaLadda upp lokala filer för API-arbetsflöden.

Läs mer

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": [
              "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": "adaptive",
            "description": "The aspect ratio of the generated video. Use 'adaptive' to let the model choose.",
            "enum": [
              "adaptive",
              "21:9",
              "16:9",
              "4:3",
              "1:1",
              "3:4",
              "9:16"
            ],
            "type": "string",
            "x-ui-component": "select"
          }
        },
        "required": [
          "model",
          "prompt",
          "image"
        ],
        "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-vänlig Promptmall

# 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`, _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. Use 'adaptive' to let the model choose.
  - Default: `"adaptive"`
  - Options: "adaptive", "21:9", "16:9", "4:3", "1:1", "3:4", "9:16"



**Required Parameters Example**:

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


**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.

Laddar...

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 16:9, 9:16, 1:1, or adaptive.
Selectable duration Produce 5s or 10s 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	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

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.

MiniMax H3 Text-to-Video — Generate video directly from a text prompt.
MiniMax H3 Reference-to-Video — Keep a subject consistent from a reference image.

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

Hailuo 02 i2v Pro

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

Hailuo 02 Standard

Hailuo 02 Standard - MiniMax's next-generation AI video model with 2.5x efficiency improvement, 85% complex instruction response rate, and industry-leading cost-effectiveness for generating high-quality videos.

Sync.so Lipsync v3

Sync.so Lipsync v3 (sync-3) is Sync Labs state-of-the-art lip-sync model, re-syncing the lips of an existing video to a new audio track with industry-leading naturalness.

VEED Lipsync

VEED Lipsync re-drives the lip movements of an existing talking-head video to match a new audio track, preserving identity, appearance and background.

LIP-SYNC

From

$0.013/SEK

Ett API för all media-AI.

Utforska alla modeller

MiniMax H3 Image-to-Video API by MiniMax

Inmatning

Utmatning

Parametrar

Kodexempel

Installera

Autentisering

HTTP-huvuden

Skicka en förfrågan

Skicka en förfrågan

Förfrågningsinnehåll

Svar

Kontrollera status

Polling-exempel

Statusvärden

Slutfört svar

Ladda upp filer

Uppladdningsexempel

Svar

Input Schema

Exempel på förfrågningsinnehåll

Output Schema

Exempelsvar

Atlas Cloud Skills

Stödda klienter

Installera

Konfigurera API Key

Funktioner

MCP Server

Stödda klienter

Installera

Konfiguration

Tillgängliga verktyg

API Schema

LLM-vänlig Promptmall

MiniMax H3 Image-to-Video

Why Choose This?

Parameters

How to Use

Pricing

Best Use Cases

Pro Tips

Notes

Related Models

Utforska Liknande Modeller

MiniMax H3 Text-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

Ett API för all media-AI.

Inmatning

Utmatning

Parametrar

Kodexempel

Installera

Autentisering

HTTP-huvuden

Skicka en förfrågan

Skicka en förfrågan

Förfrågningsinnehåll

Svar

Kontrollera status

Polling-exempel

Statusvärden

Slutfört svar

Ladda upp filer

Uppladdningsexempel

Svar