minimax/h3-developer/reference-to-video

图生视频

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MiniMax H3 Developer Reference-to-Video API by MiniMax

minimax/h3-developer/reference-to-video

Reference-to-video

MiniMax H3-Developer self-hosted reference-to-video: generate a video that keeps the subject from one or more reference images/videos, driven by a text prompt, with generated audio. Supports 480P768P/2K.

MiniMax H3 Developer Reference-to-Video 由 MiniMax 开发。Atlas Cloud（由 Atlas Cloud AI LLC 运营）仅提供接入服务，并不拥有该模型。所有商标均归其各自所有者所有。

输入

提示词 *

参考素材 *(0/9)

可拖拽文件至此，或点击上传

MAX:9

分辨率 *

时长 *

画面比例

Prompt expansion

输出

空闲

生成的视频将在这里显示

配置参数后点击运行开始生成

每次运行将花费 $0.015。$10 可运行约 666 次。

你可以继续：

Seedance 2.0 Kling v3 Vidu Wan2.7

参数

代码示例
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-developer/reference-to-video",  # Required. model name
    "prompt": "A beautiful sunset over the ocean with gentle waves",  # Required. The text prompt describing the video to generate
    "refers": [
        {
            "url": "example_url",
            "type": "example_type"
        }
    ],  # Required. Reference materials that anchor the generation
    "resolution": "768P",  # Required. The resolution of the generated video. options: 480P | 768P | 1440p-sr | 4k-sr
    "duration": 8,  # Required. The duration of the generated video in seconds
    "ratio": "adaptive",  # The aspect ratio of the generated video
    "prompt_expansion": False,  # Whether to expand the prompt for better results
}

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()

安装

安装所需的依赖包。

pip install requests

认证

所有 API 请求需要通过 API Key 进行认证。您可以在 Atlas Cloud 控制台获取 API Key。

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

HTTP 请求头

import os

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

保护好您的 API Key

切勿在客户端代码或公开仓库中暴露您的 API Key。请使用环境变量或后端代理。

提交请求

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())

提交请求

提交一个异步生成请求。API 返回一个 prediction ID，您可以用它来检查状态和获取结果。

POST/api/v1/model/generateVideo

请求体

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-developer/reference-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']}")

响应

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

检查状态

轮询 prediction 端点以检查请求的当前状态。

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

轮询示例

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)

状态值

processing请求仍在处理中。

completed生成完成，输出可用。

succeeded生成成功，输出可用。

failed生成失败，请检查 error 字段。

完成响应

{
  "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"
  }
}

上传文件

将文件上传到 Atlas Cloud 存储，获取可在 API 请求中使用的 URL。使用 multipart/form-data 上传。

POST/api/v1/model/uploadMedia

上传示例

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}")

响应

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

Input Schema

以下参数在请求体中被接受。

总计: 7必填: 5可选: 2

modelstringrequired

model name

Default: "minimax/h3-developer/reference-to-video"

promptstringrequired

The text prompt describing the video to generate.

refersarray[object]required

Reference materials that anchor the generation. Any mix of reference images, videos, and audio, each as a public URL. At least one image OR video is required (audio alone is not allowed). Image formats: png, jpeg, jpg, webp; video: mp4, mov; audio: mp3, wav. 'type' is optional — when omitted it is inferred from the URL extension.

Min items: 1

urlstringrequired

Public URL of the reference image / video / audio.

typestring

Media kind. Optional; inferred from the URL when omitted.

imagevideoaudio

resolutionstringrequired

The resolution of the generated video.

Default: "768P"

480P768P1440p-sr4k-sr

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"

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

prompt_expansionboolean

Whether to expand the prompt for better results.

Default: false

请求体示例

{
  "model": "minimax/h3-developer/reference-to-video",
  "prompt": "A beautiful landscape",
  "refers": [
    {
      "url": "example_url",
      "type": "example_type"
    }
  ],
  "resolution": "768P",
  "duration": 8,
  "ratio": "adaptive",
  "prompt_expansion": false
}

Output Schema

API 返回包含生成输出 URL 的 prediction 响应。

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.

响应示例

{
  "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 将 400+ AI 模型直接集成到您的 AI 编程助手中。一条命令安装，即可用自然语言生成图像、视频和与 LLM 对话。

支持的客户端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支持的客户端

安装

npx skills add AtlasCloudAI/atlas-cloud-skills

设置 API Key

从 Atlas Cloud 控制台获取 API Key，并将其设置为环境变量。

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

功能

安装后，您可以在 AI 助手中使用自然语言访问所有 Atlas Cloud 模型。

图像生成使用 Nano Banana 2、Z-Image 等模型生成图像。

视频创作使用 Kling、Vidu、Veo 等模型从文本或图像创建视频。

LLM 对话与 Qwen、DeepSeek 等大语言模型对话。

媒体上传上传本地文件用于图像编辑和图生视频工作流。

MCP Server

Atlas Cloud MCP Server 通过 Model Context Protocol 将您的 IDE 与 400+ AI 模型连接。支持任何兼容 MCP 的客户端。

支持的客户端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支持的客户端

安装

npx -y atlascloud-mcp

配置

将以下配置添加到您的 IDE 的 MCP 设置文件中。

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

可用工具

atlas_generate_image从文本提示生成图像。

atlas_generate_video从文本或图像创建视频。

atlas_chat与大语言模型对话。

atlas_list_models浏览 400+ 可用 AI 模型。

atlas_quick_generate一步式内容创建，自动选择最佳模型。

atlas_upload_media上传本地文件用于 API 工作流。

了解更多

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "minimax/h3-developer/reference-to-video"
          },
          "prompt": {
            "description": "The text prompt describing the video to generate.",
            "type": "string"
          },
          "refers": {
            "description": "Reference materials that anchor the generation. Any mix of reference images, videos, and audio, each as a public URL. At least one image OR video is required (audio alone is not allowed). Image formats: png, jpeg, jpg, webp; video: mp4, mov; audio: mp3, wav. 'type' is optional — when omitted it is inferred from the URL extension.",
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "url": {
                  "type": "string",
                  "description": "Public URL of the reference image / video / audio.",
                  "x-ui-component": "uploaders"
                },
                "type": {
                  "type": "string",
                  "description": "Media kind. Optional; inferred from the URL when omitted.",
                  "enum": [
                    "image",
                    "video",
                    "audio"
                  ]
                }
              },
              "required": [
                "url"
              ]
            },
            "minItems": 1,
            "x-ui-component": "uploaders"
          },
          "resolution": {
            "default": "768P",
            "description": "The resolution of the generated video.",
            "enum": [
              "480P",
              "768P",
              "1440p-sr",
              "4k-sr"
            ],
            "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",
              "21:9",
              "16:9",
              "4:3",
              "1:1",
              "3:4",
              "9:16"
            ],
            "type": "string",
            "x-ui-component": "select"
          },
          "prompt_expansion": {
            "default": false,
            "description": "Whether to expand the prompt for better results.",
            "type": "boolean",
            "x-ui-component": "switch"
          }
        },
        "required": [
          "model",
          "prompt",
          "resolution",
          "duration",
          "refers"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "refers",
          "resolution",
          "duration",
          "ratio",
          "prompt_expansion"
        ]
      },
      "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"
          },
          "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 友好的提示词模板

# minimax/h3-developer/reference-to-video

> MiniMax H3-Developer self-hosted reference-to-video: generate a video that keeps the subject from one or more reference images/videos, driven by a text prompt, with generated audio. Supports 480P768P/2K.


## 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-developer/reference-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-developer/reference-to-video"`

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

- **`refers`** (`array[object]`, _required_):
  Reference materials that anchor the generation. Any mix of reference images, videos, and audio, each as a public URL. At least one image OR video is required (audio alone is not allowed). Image formats: png, jpeg, jpg, webp; video: mp4, mov; audio: mp3, wav. 'type' is optional — when omitted it is inferred from the URL extension.
  - Min items: 1
  - Item properties:
    - **`url`** (`string`, _required_):
      Public URL of the reference image / video / audio.

    - **`type`** (`string`, _optional_):
      Media kind. Optional; inferred from the URL when omitted.
      - Options: "image", "video", "audio"


- **`resolution`** (`string`, _required_):
  The resolution of the generated video.
  - Default: `"768P"`
  - Options: "480P", "768P", "1440p-sr", "4k-sr"

- **`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", "21:9", "16:9", "4:3", "1:1", "3:4", "9:16"

- **`prompt_expansion`** (`boolean`, _optional_):
  Whether to expand the prompt for better results.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "minimax/h3-developer/reference-to-video",
  "prompt": "",
  "resolution": "768P",
  "duration": 8,
  "refers": [
    {
      "url": "",
      "type": "image"
    }
  ]
}
```


**Full Example**:

```json
{
  "model": "minimax/h3-developer/reference-to-video",
  "prompt": "",
  "refers": [
    {
      "url": "",
      "type": "image"
    }
  ],
  "resolution": "768P",
  "duration": 8,
  "ratio": "adaptive",
  "prompt_expansion": false
}
```


### 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”).

- **`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": "",
  "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-developer/reference-to-video",
  "prompt": "",
  "refers": [
    {
      "url": "",
      "type": "image"
    }
  ],
  "resolution": "768P",
  "duration": 8,
  "ratio": "adaptive",
  "prompt_expansion": false
}'

# 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-developer/reference-to-video)

The race car driver raises a hand in celebration of the victory.

加载中...

MiniMax H3-Developer Reference-to-Video

MiniMax H3-Developer Reference-to-Video keeps a subject consistent — with sound. Provide one or more reference materials (images and/or videos of a person, animal, or object) plus a prompt. Generate native 480P or 768P video, or select an ESR tier to enhance the output to a 1080P, 1440P, or 4K short edge while preserving the referenced subject's identity, complete with a synchronized soundtrack generated from the described scene.

Why Choose This?

Subject-consistent generation Keep the referenced subject's identity across the whole shot.
Multi-modal references Drive generation with images and/or videos in a single request.
Native audio A matching soundtrack is generated alongside the video — no separate audio input needed.
Enhanced resolution output Generate native 480P or 768P video, or use ESR for a 1080P, 1440P, or 4K short edge.
Selectable duration Produce 5–15s clips.

Parameters

Parameter	Required	Description
prompt	No	Text description of the desired video while keeping the referenced subject's identity
refers	Yes	Array of reference materials. Each item is an object `{ "url": string, "type": "image" \| "video" \| "audio" }`. `type` is inferred from the URL extension when omitted. At least one image or video is required; audio alone is rejected
resolution	No	Video resolution. Available options: `768P` (default), `480P`, `1080p-esr`, `1440p-esr`, `4k-esr`. ESR tiers generate a native 768P source and enhance the output to the requested short edge.
duration	No	Duration of the generated video in seconds. Integer between 5 and 15 (default 5)
prompt_expansion	No	Enable AI prompt expansion via H3 Context-IR. When `true`, your prompt is first expanded into a rich, structured description (shot, soundscape, music) before generation, and an additional per-token Context-IR fee applies. Default `false`: the prompt is used as-is with no extra charge.

How to Use

Add your reference materials — pass one or more refers entries (image and/or video URLs).
Write your prompt — describe the desired scene, action, and sound while keeping the subject.
Set resolution and duration — balance quality against generation speed.
(Optional) Tune seed and steps — fix the seed for reproducibility, raise steps for quality.
Run — submit and download your video.

Best Use Cases

Character Consistency — Keep the same character across multiple shots.
Product Showcases — Feature a specific product while varying the scene.
Brand & IP — Reuse a mascot or signature subject in new content.
Storytelling — Build a narrative around a consistent subject, with matching sound.

Pro Tips

Use clear, high-quality reference images or videos of the subject.
Provide at least one image or video reference; audio-only requests are rejected.
Describe the scene's audio (traffic, music, crowd) to shape the generated soundtrack.
Keep the prompt focused on the scene and action; the subject's identity comes from the references.

Notes

At least one image or video reference is required.
The prompt is optional but strongly recommended.
ESR tiers add an enhancement stage after native generation and may take longer than native resolutions.
Ensure all reference URLs are publicly accessible.
Generation is asynchronous — submit, then poll for the finished video.