tencent/image/upscaler

image-to-image

Tencent Image Upscaler API

tencent/image/upscaler

Upscaler

Tencent Image Upscaler (MPS advanced super-resolution)

Tencent Image Upscaler is developed by Tencent. 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

Image *

You can drag and drop a file or click to upload

MAX:1

Type

Mode

Width

Height

Long edge

Short edge

Encode format

Encode quality

OUTPUT

Idle

Your generated images will appear here

Configure your settings and click Run to get started

Your request will cost $0.024 per run. For $10 you can run this model approximately 416 times.

Here's what you can do next:

Image-to-Video Image-to-Image

Parameters

Code Example
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": "tencent/image/upscaler",  # Required. model name
    "image_url": "example_value",  # Required. URL of the source image to enhance
    "type": "ultra",  # Advanced super-resolution type. options: standard | super | ultra | fidelity
    "mode": "percent",  # Super-resolution mode. options: percent | aspect | fixed
    "percent": 2,  # Scale factor for mode=percent. (min: 1, max: 10)
    "width": 1,  # Target width in pixels (for mode=aspect / fixed). (min: 1)
    "height": 1,  # Target height in pixels (for mode=aspect / fixed). (min: 1)
    "long_side": 1,  # Target long-edge in pixels (for mode=aspect / fixed). (min: 1)
    "short_side": 1,  # Target short-edge in pixels (for mode=aspect / fixed). (min: 1)
    "encode_format": "JPEG",  # Output image encode format. options: JPEG | PNG | WEBP | BMP
    "encode_quality": 80,  # Output image encode quality (1-100). (min: 1, max: 100)
    "output_dir": "example_value",  # Optional COS output directory (advanced)
}

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()

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/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())

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/generateImage

Request Body

import requests

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

data = {
    "model": "tencent/image/upscaler",
    "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']}")

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.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "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: 12Required: 2Optional: 10

modelstringrequired

model name

Default: "tencent/image/upscaler"

image_urlstringrequired

URL of the source image to enhance. Must be a publicly accessible HTTP(S) URL (user-uploaded or third-party-model output are both supported). Supported formats: .jpg/.jpeg/.png.

typestring

Advanced super-resolution type. standard = generic upscale (fastest ~5s); super = AIGC scenes (~15s); ultra = more detail enhancement (~15s); fidelity = ultra high fidelity for e-commerce (~30s).

Default: "ultra"

standardsuperultrafidelity

modestring

Super-resolution mode. percent = scale by a factor (use percent); aspect = keep aspect ratio, scale to fit given dimensions; fixed = force exact dimensions.

Default: "percent"

percentaspectfixed

percentnumber

Scale factor for mode=percent. 1.0 = keep resolution (enhance only), 2.0 = 2x. Range 1.0~10.0.

Default: 2Min: 1Max: 10

widthinteger

Target width in pixels (for mode=aspect / fixed).

Min: 1

heightinteger

Target height in pixels (for mode=aspect / fixed).

Min: 1

long_sideinteger

Target long-edge in pixels (for mode=aspect / fixed). Alternative to width/height.

Min: 1

short_sideinteger

Target short-edge in pixels (for mode=aspect / fixed). Alternative to width/height.

Min: 1

encode_formatstring

Output image encode format.

Default: "JPEG"

JPEGPNGWEBPBMP

encode_qualityinteger

Output image encode quality (1-100). Applies to lossy formats.

Default: 80Min: 1Max: 100

output_dirstring

Optional COS output directory (advanced). Defaults to an auto-generated directory.

Example Request Body

{
  "model": "tencent/image/upscaler",
  "image_url": "example_image_url",
  "type": "ultra",
  "mode": "percent",
  "percent": 2,
  "encode_format": "JPEG",
  "encode_quality": 80
}

Output Schema

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

created_atstring

ISO timestamp of when the request was created.

idstring

Unique identifier for the prediction.

modelstring

Model ID used for the prediction.

outputsarray

Array of URLs to the enhanced image(s).

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.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 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": "tencent/image/upscaler"
          },
          "image_url": {
            "description": "URL of the source image to enhance. Must be a publicly accessible HTTP(S) URL (user-uploaded or third-party-model output are both supported). Supported formats: .jpg/.jpeg/.png.",
            "type": "string",
            "x-accept": "image/*",
            "x-ui-component": "uploader"
          },
          "type": {
            "type": "string",
            "description": "Advanced super-resolution type. standard = generic upscale (fastest ~5s); super = AIGC scenes (~15s); ultra = more detail enhancement (~15s); fidelity = ultra high fidelity for e-commerce (~30s).",
            "enum": [
              "standard",
              "super",
              "ultra",
              "fidelity"
            ],
            "default": "ultra",
            "x-ui-component": "select"
          },
          "mode": {
            "type": "string",
            "description": "Super-resolution mode. percent = scale by a factor (use percent); aspect = keep aspect ratio, scale to fit given dimensions; fixed = force exact dimensions.",
            "enum": [
              "percent",
              "aspect",
              "fixed"
            ],
            "default": "percent",
            "x-ui-component": "select"
          },
          "percent": {
            "type": "number",
            "description": "Scale factor for mode=percent. 1.0 = keep resolution (enhance only), 2.0 = 2x. Range 1.0~10.0.",
            "default": 2,
            "minimum": 1,
            "maximum": 10,
            "step": 0.1,
            "x-ui-component": "slider"
          },
          "width": {
            "type": "integer",
            "description": "Target width in pixels (for mode=aspect / fixed).",
            "minimum": 1
          },
          "height": {
            "type": "integer",
            "description": "Target height in pixels (for mode=aspect / fixed).",
            "minimum": 1
          },
          "long_side": {
            "type": "integer",
            "description": "Target long-edge in pixels (for mode=aspect / fixed). Alternative to width/height.",
            "minimum": 1
          },
          "short_side": {
            "type": "integer",
            "description": "Target short-edge in pixels (for mode=aspect / fixed). Alternative to width/height.",
            "minimum": 1
          },
          "encode_format": {
            "type": "string",
            "description": "Output image encode format.",
            "enum": [
              "JPEG",
              "PNG",
              "WEBP",
              "BMP"
            ],
            "default": "JPEG",
            "x-ui-component": "select"
          },
          "encode_quality": {
            "type": "integer",
            "description": "Output image encode quality (1-100). Applies to lossy formats.",
            "default": 80,
            "minimum": 1,
            "maximum": 100
          },
          "output_dir": {
            "type": "string",
            "description": "Optional COS output directory (advanced). Defaults to an auto-generated directory."
          }
        },
        "allOf": [
          {
            "if": {
              "properties": {
                "mode": {
                  "const": "percent"
                }
              },
              "required": [
                "mode"
              ]
            },
            "then": {
              "required": [
                "percent"
              ],
              "properties": {
                "width": {
                  "x-hidden": true
                },
                "height": {
                  "x-hidden": true
                },
                "long_side": {
                  "x-hidden": true
                },
                "short_side": {
                  "x-hidden": true
                }
              }
            },
            "else": {
              "properties": {
                "percent": {
                  "x-hidden": true
                }
              }
            }
          }
        ],
        "required": [
          "model",
          "image_url"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "image_url",
          "type",
          "mode",
          "percent",
          "width",
          "height",
          "long_side",
          "short_side",
          "encode_format",
          "encode_quality"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "created_at": {
            "description": "ISO timestamp of when the request was created.",
            "format": "date-time",
            "type": "string"
          },
          "id": {
            "description": "Unique identifier for the prediction.",
            "type": "string"
          },
          "model": {
            "description": "Model ID used for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the enhanced image(s).",
            "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/generateImage": {
      "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"
    },
    "/api/v1/model/result/{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"
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "model": "tencent/image/upscaler",
  "description": "Tencent Image Upscaler Ultra (MPS advanced super-resolution)"
}

LLM-Friendly Prompt Template

# tencent/image/upscaler

> Tencent Image Upscaler (MPS advanced super-resolution)


## 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**: `tencent/image/upscaler`


## 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: `"tencent/image/upscaler"`

- **`image_url`** (`string`, _required_):
  URL of the source image to enhance. Must be a publicly accessible HTTP(S) URL (user-uploaded or third-party-model output are both supported). Supported formats: .jpg/.jpeg/.png.

- **`type`** (`string`, _optional_):
  Advanced super-resolution type. standard = generic upscale (fastest ~5s); super = AIGC scenes (~15s); ultra = more detail enhancement (~15s); fidelity = ultra high fidelity for e-commerce (~30s).
  - Default: `"ultra"`
  - Options: "standard", "super", "ultra", "fidelity"

- **`mode`** (`string`, _optional_):
  Super-resolution mode. percent = scale by a factor (use percent); aspect = keep aspect ratio, scale to fit given dimensions; fixed = force exact dimensions.
  - Default: `"percent"`
  - Options: "percent", "aspect", "fixed"

- **`percent`** (`number`, _optional_):
  Scale factor for mode=percent. 1.0 = keep resolution (enhance only), 2.0 = 2x. Range 1.0~10.0.
  - Default: `2`
  - Min: 1
  - Max: 10

- **`width`** (`integer`, _optional_):
  Target width in pixels (for mode=aspect / fixed).
  - Min: 1

- **`height`** (`integer`, _optional_):
  Target height in pixels (for mode=aspect / fixed).
  - Min: 1

- **`long_side`** (`integer`, _optional_):
  Target long-edge in pixels (for mode=aspect / fixed). Alternative to width/height.
  - Min: 1

- **`short_side`** (`integer`, _optional_):
  Target short-edge in pixels (for mode=aspect / fixed). Alternative to width/height.
  - Min: 1

- **`encode_format`** (`string`, _optional_):
  Output image encode format.
  - Default: `"JPEG"`
  - Options: "JPEG", "PNG", "WEBP", "BMP"

- **`encode_quality`** (`integer`, _optional_):
  Output image encode quality (1-100). Applies to lossy formats.
  - Default: `80`
  - Min: 1
  - Max: 100



**Required Parameters Example**:

```json
{
  "model": "tencent/image/upscaler",
  "image_url": ""
}
```


**Full Example**:

```json
{
  "model": "tencent/image/upscaler",
  "image_url": "",
  "type": "ultra",
  "mode": "percent",
  "percent": 2,
  "width": 1,
  "height": 1,
  "long_side": 1,
  "short_side": 1,
  "encode_format": "JPEG",
  "encode_quality": 80
}
```


### Output Schema

The API returns the following output format:


- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created.

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

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

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the enhanced image(s).

- **`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/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "tencent/image/upscaler",
  "image_url": "",
  "type": "ultra",
  "mode": "percent",
  "percent": 2,
  "width": 1,
  "height": 1,
  "long_side": 1,
  "short_side": 1,
  "encode_format": "JPEG",
  "encode_quality": 80
}'

# 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/tencent/image/upscaler)