tripo-h3.1/image-to-3d

圖生3D

Tripo H3.1 Image-to 3D API

tripo-h3.1/image-to-3d

Image-to-3d

Generate high-quality 3D models from a single image using Tripo H3.1.

輸入

Image url *

可拖曳檔案至此，或點擊上傳

MAX:1

Texture

Pbr

Texture quality

Geometry quality

Texture alignment

Orientation

進階設定

輸出

閒置

生成的 3D 模型將顯示於此

設定參數後點擊 Run 開始生成

每次執行將花費 $0.1。$10 可執行約 100 次。

參數

程式碼範例
import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "tripo-h3.1/image-to-3d",  # Required. model name
    "image_url": "example_value",  # Required. URL of the input image used to create the 3D model
    "face_limit": 1000,  # Target mesh face count. (min: 1000, max: 2000000)
    "texture": True,  # Enable texture generation
    "pbr": True,  # Generate PBR (Physically Based Rendering) materials
    "texture_quality": "standard",  # Texture resolution level. options: standard | detailed
    "geometry_quality": "standard",  # Geometry quality level. options: standard | detailed
    "texture_alignment": "original_image",  # How the texture is aligned to the mesh. options: original_image | geometry
    "orientation": "default",  # Model orientation strategy. options: default | align_image
    "model_seed": 1,  # Seed for geometry generation reproducibility
    "texture_seed": 1,  # Seed for texture generation reproducibility
    "auto_size": False,  # Auto-scale the model to real-world dimensions (meters)
    "quad": False,  # Generate a quad mesh topology
}

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"] == "completed":
            print("Generated image:", 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)

image_url = check_status()

安裝

安裝所需的相依套件。

pip install requests

驗證

所有 API 請求都需要透過 API Key 進行認證。您可以在 Atlas Cloud 控制台取得 API Key。

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

HTTP 標頭

import os

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

保護好您的 API Key

切勿在客戶端程式碼或公開儲存庫中暴露您的 API Key。請使用環境變數或後端代理。

提交請求

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
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())

提交請求

提交一個非同步生成請求。API 會傳回一個 prediction ID，您可以用它來檢查狀態與取得結果。

POST/api/v1/model/generateImage

請求主體

import requests

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

data = {
    "model": "tripo-h3.1/image-to-3d",
    "prompt": "A beautiful landscape with mountains and lake"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

回應

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

檢查狀態

輪詢 prediction 端點以檢查請求的當前狀態。

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

輪詢範例

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)

狀態值

processing請求仍在處理中。

completed生成完成，輸出可用。

succeeded生成成功，輸出可用。

failed生成失敗，請檢查 error 欄位。

完成回應

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

上傳檔案

將檔案上傳到 Atlas Cloud 儲存空間，取得可在 API 請求中使用的 URL。使用 multipart/form-data 上傳。

POST/api/v1/model/uploadMedia

上傳範例

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}")

回應

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

Input Schema

以下參數在請求主體中被接受。

總計: 13必填: 2選填: 11

modelstringrequired

model name

Default: "tripo-h3.1/image-to-3d"

image_urlstringrequired

URL of the input image used to create the 3D model.

face_limitinteger

Target mesh face count. If unset, the model determines it adaptively.

Min: 1000Max: 2000000

textureboolean

Enable texture generation.

Default: true

pbrboolean

Generate PBR (Physically Based Rendering) materials.

Default: true

texture_qualitystring

Texture resolution level.

Default: "standard"

standarddetailed

geometry_qualitystring

Geometry quality level.

Default: "standard"

standarddetailed

texture_alignmentstring

How the texture is aligned to the mesh.

Default: "original_image"

original_imagegeometry

orientationstring

Model orientation strategy.

Default: "default"

defaultalign_image

model_seedinteger

Seed for geometry generation reproducibility.

texture_seedinteger

Seed for texture generation reproducibility.

auto_sizeboolean

Auto-scale the model to real-world dimensions (meters).

Default: false

quadboolean

Generate a quad mesh topology. Outputs FBX when enabled.

Default: false

範例請求主體

{
  "model": "tripo-h3.1/image-to-3d",
  "image_url": "example_image_url",
  "texture": true,
  "pbr": true,
  "texture_quality": "standard",
  "geometry_quality": "standard",
  "texture_alignment": "original_image",
  "orientation": "default",
  "auto_size": false,
  "quad": false
}

Output Schema

API 傳回包含生成輸出 URL 的 prediction 回應。

modelstring

model name

Default: "tripo-h3.1/image-to-3d"

created_atstring

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

idstring

Unique identifier for the prediction.

outputsarray

Array of URLs to the generated 3D model files (GLB/FBX and variants), preview image last. Null when status is not completed.

filesarray

Structured per-output metadata (URL, type, content_type, file_name, file_size) for each generated 3D file.

thumbnailstring

Preview render image URL of the generated 3D model.

statusstring

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

範例回應

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills 將 400+ AI 模型直接整合到您的 AI 程式碼助手中。一條命令安裝，即可用自然語言生成圖片、影片，以及與 LLM 對話。

支援的客戶端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支援的客戶端

安裝

npx skills add AtlasCloudAI/atlas-cloud-skills

設定 API Key

從 Atlas Cloud 控制台取得 API Key，並將其設定為環境變數。

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

功能

安裝完成後，您可以在 AI 助手中使用自然語言存取所有 Atlas Cloud 模型。

圖片生成使用 Nano Banana 2、Z-Image 等模型生成圖片。

影片創作使用 Kling、Vidu、Veo 等從文字或圖片創建影片。

LLM 對話與 Qwen、DeepSeek 及其他大型語言模型對話。

媒體上傳上傳本機檔案用於圖片編輯和圖生影片工作流程。

MCP Server

Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 400+ AI 模型連接。支援任何相容 MCP 的客戶端。

支援的客戶端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支援的客戶端

安裝

npx -y atlascloud-mcp

設定

將以下設定新增到您的 IDE 的 MCP 設定檔中。

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

可用工具

atlas_generate_image根據文字提示生成圖片。

atlas_generate_video從文字或圖片創建影片。

atlas_chat與大型語言模型對話。

atlas_list_models瀏覽 400+ 可用 AI 模型。

atlas_quick_generate一步式內容創建，自動選擇最佳模型。

atlas_upload_media上傳本機檔案用於 API 工作流程。

了解更多

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "paths": {
    "/api/v1/model/generateImage": {
      "post": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        },
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        },
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "schema": {
              "type": "string",
              "description": "Request ID"
            },
            "required": true
          }
        ]
      },
      "x-api-name": "model_result"
    }
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "tripo-h3.1/image-to-3d"
          },
          "image_url": {
            "description": "URL of the input image used to create the 3D model.",
            "type": "string",
            "x-format": "image",
            "x-ui-component": "uploader"
          },
          "face_limit": {
            "description": "Target mesh face count. If unset, the model determines it adaptively.",
            "type": "integer",
            "minimum": 1000,
            "maximum": 2000000
          },
          "texture": {
            "description": "Enable texture generation.",
            "type": "boolean",
            "default": true
          },
          "pbr": {
            "description": "Generate PBR (Physically Based Rendering) materials.",
            "type": "boolean",
            "default": true
          },
          "texture_quality": {
            "description": "Texture resolution level.",
            "type": "string",
            "default": "standard",
            "enum": [
              "standard",
              "detailed"
            ],
            "x-ui-component": "select"
          },
          "geometry_quality": {
            "description": "Geometry quality level.",
            "type": "string",
            "default": "standard",
            "enum": [
              "standard",
              "detailed"
            ],
            "x-ui-component": "select"
          },
          "texture_alignment": {
            "description": "How the texture is aligned to the mesh.",
            "type": "string",
            "default": "original_image",
            "enum": [
              "original_image",
              "geometry"
            ],
            "x-ui-component": "select"
          },
          "orientation": {
            "description": "Model orientation strategy.",
            "type": "string",
            "default": "default",
            "enum": [
              "default",
              "align_image"
            ],
            "x-ui-component": "select"
          },
          "model_seed": {
            "description": "Seed for geometry generation reproducibility.",
            "type": "integer"
          },
          "texture_seed": {
            "description": "Seed for texture generation reproducibility.",
            "type": "integer"
          },
          "auto_size": {
            "description": "Auto-scale the model to real-world dimensions (meters).",
            "type": "boolean",
            "default": false
          },
          "quad": {
            "description": "Generate a quad mesh topology. Outputs FBX when enabled.",
            "type": "boolean",
            "default": false
          }
        },
        "required": [
          "model",
          "image_url"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "image_url",
          "texture",
          "pbr",
          "texture_quality",
          "geometry_quality",
          "texture_alignment",
          "orientation",
          "face_limit",
          "model_seed",
          "texture_seed",
          "auto_size",
          "quad"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "tripo-h3.1/image-to-3d"
          },
          "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.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the generated 3D model files (GLB/FBX and variants), preview image last. Null when status is not completed.",
            "items": {
              "type": "string"
            },
            "nullable": true,
            "type": "array"
          },
          "files": {
            "description": "Structured per-output metadata (URL, type, content_type, file_name, file_size) for each generated 3D file.",
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "url": {
                  "type": "string"
                },
                "type": {
                  "type": "string"
                },
                "content_type": {
                  "type": "string"
                },
                "file_name": {
                  "type": "string"
                },
                "file_size": {
                  "type": "integer"
                }
              }
            }
          },
          "thumbnail": {
            "description": "Preview render image URL of the generated 3D model.",
            "type": "string"
          },
          "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"
      }
    }
  }
}

LLM 友善的提示詞範本

# tripo-h3.1/image-to-3d

> Generate high-quality 3D models from a single image using Tripo H3.1.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — 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**: `tripo-h3.1/image-to-3d`


## 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: `"tripo-h3.1/image-to-3d"`

- **`image_url`** (`string`, _required_):
  URL of the input image used to create the 3D model.

- **`texture`** (`boolean`, _optional_):
  Enable texture generation.
  - Default: `true`

- **`pbr`** (`boolean`, _optional_):
  Generate PBR (Physically Based Rendering) materials.
  - Default: `true`

- **`texture_quality`** (`string`, _optional_):
  Texture resolution level.
  - Default: `"standard"`
  - Options: "standard", "detailed"

- **`geometry_quality`** (`string`, _optional_):
  Geometry quality level.
  - Default: `"standard"`
  - Options: "standard", "detailed"

- **`texture_alignment`** (`string`, _optional_):
  How the texture is aligned to the mesh.
  - Default: `"original_image"`
  - Options: "original_image", "geometry"

- **`orientation`** (`string`, _optional_):
  Model orientation strategy.
  - Default: `"default"`
  - Options: "default", "align_image"

- **`face_limit`** (`integer`, _optional_):
  Target mesh face count. If unset, the model determines it adaptively.
  - Min: 1000
  - Max: 2000000

- **`model_seed`** (`integer`, _optional_):
  Seed for geometry generation reproducibility.

- **`texture_seed`** (`integer`, _optional_):
  Seed for texture generation reproducibility.

- **`auto_size`** (`boolean`, _optional_):
  Auto-scale the model to real-world dimensions (meters).
  - Default: `false`

- **`quad`** (`boolean`, _optional_):
  Generate a quad mesh topology. Outputs FBX when enabled.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "tripo-h3.1/image-to-3d",
  "image_url": ""
}
```


**Full Example**:

```json
{
  "model": "tripo-h3.1/image-to-3d",
  "image_url": "",
  "texture": true,
  "pbr": true,
  "texture_quality": "standard",
  "geometry_quality": "standard",
  "texture_alignment": "original_image",
  "orientation": "default",
  "face_limit": 1000,
  "model_seed": 0,
  "texture_seed": 0,
  "auto_size": false,
  "quad": false
}
```


### Output Schema

The API returns the following output format:


- **`model`** (`string`, _optional_):
  model name
  - Default: `"tripo-h3.1/image-to-3d"`

- **`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.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated 3D model files (GLB/FBX and variants), preview image last. Null when status is not completed.

- **`files`** (`array[object]`, _optional_):
  Structured per-output metadata (URL, type, content_type, file_name, file_size) for each generated 3D file.
  - Item properties:
    - **`url`** (`string`, _optional_):

    - **`type`** (`string`, _optional_):

    - **`content_type`** (`string`, _optional_):

    - **`file_name`** (`string`, _optional_):

    - **`file_size`** (`integer`, _optional_):


- **`thumbnail`** (`string`, _optional_):
  Preview render image URL of the generated 3D model.

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

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



**Example Response**:

```json
{
  "model": "tripo-h3.1/image-to-3d",
  "created_at": "",
  "id": "",
  "outputs": [
    ""
  ],
  "files": [
    {
      "url": "",
      "type": "",
      "content_type": "",
      "file_name": "",
      "file_size": 0
    }
  ],
  "thumbnail": "",
  "status": "",
  "urls": {}
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "tripo-h3.1/image-to-3d",
  "image_url": "",
  "texture": true,
  "pbr": true,
  "texture_quality": "standard",
  "geometry_quality": "standard",
  "texture_alignment": "original_image",
  "orientation": "default",
  "face_limit": 1000,
  "model_seed": 0,
  "texture_seed": 0,
  "auto_size": false,
  "quad": 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/tripo-h3.1/image-to-3d)

No 3D output

載入中...

Tripo H3.1 Image-to-3D

Tripo H3.1 Image-to-3D reconstructs a textured 3D model from a single input image. It produces a mesh (GLB, or FBX when quad topology is requested) with optional PBR materials and a preview render — ideal for turning product photos, concept art, or references into 3D assets.

🌟 Key Features

🖼 Image-to-3D

Reconstruct a complete 3D mesh from one input image.

🎨 Textures & PBR Materials

Optionally generate textures and Physically Based Rendering (PBR) materials for production-ready shading.

🎯 Texture Alignment & Orientation

Align the texture to the original image or to the geometry, and control model orientation.

🔺 Adaptive or Fixed Face Count

Let the model choose the mesh density adaptively, or cap it with face_limit for lighter assets.

📦 Multiple Output Formats

Receive GLB by default, or FBX when quad is enabled, alongside base and PBR model variants.

⚙️ Parameters

Parameter	Required	Description
`image_url`	✅	URL of the input image used to create the 3D model.
`texture`	❌	Enable texture generation (default: `true`).
`pbr`	❌	Generate PBR materials (default: `true`).
`texture_quality`	❌	Texture resolution level: `standard` (default) or `detailed`.
`geometry_quality`	❌	Geometry quality level: `standard` (default) or `detailed`.
`texture_alignment`	❌	Texture alignment: `original_image` (default) or `geometry`.
`orientation`	❌	Model orientation: `default` or `align_image`.
`face_limit`	❌	Target polygon count. Adaptive if unset.
`model_seed`	❌	Seed for geometry generation reproducibility.
`texture_seed`	❌	Seed for texture generation reproducibility.
`auto_size`	❌	Auto-scale the model to real-world dimensions in meters (default: `false`).
`quad`	❌	Generate a quad mesh topology; outputs FBX (default: `false`).

💲 Pricing

Configuration	Price
Without textures	$0.22
With standard textures	$0.33
With HD textures	$0.44
+ Detailed geometry (`geometry_quality: detailed`)	+$0.22
+ Quad mesh (`quad: true`)	+$0.055

💡 Input Tips

Use a clear, well-lit image with the subject centered and a clean background.
Set texture_alignment: original_image to keep the reconstructed texture faithful to the photo.
Use orientation: align_image when the model should face the same direction as the input.
Enable pbr for realistic materials in engines that support PBR shading.
Set face_limit to keep meshes lightweight for real-time / mobile use.

🎯 Use Cases

Product Digitization — Turn product photos into interactive 3D previews.
Game & AR Assets — Convert concept art or references into usable meshes.
Photogrammetry Alternative — Generate 3D from a single image without a full capture rig.
Rapid Prototyping — Explore 3D reconstructions before manual modeling.

📝 Notes

image_url is the only required field (besides model). Our platform also accepts image as an alias.
Output model files are returned in outputs (GLB/FBX and variants); the preview image is in thumbnail and appended to outputs last.
Structured per-file metadata (type, content_type, size) is available in files.
Task status values: created, processing, completed, failed.

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