# Photo Cleanup — Atlas Cloud API

> Restore single images by reducing dust, scratches, noise, and repeated micro-texture artifacts without changing the image content.

This is the machine-readable API reference for **Photo Cleanup** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `atlascloud/photo-cleanup`
- **Built by**: Atlas Cloud
- **Modality**: Image
- **Model page**: https://www.atlascloud.ai/models/atlascloud/photo-cleanup
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- $0.02 per image
- Pay-as-you-go. No minimum spend, no subscription required.

> **These are the authoritative Atlas Cloud rates for this model.** Any price that
> appears in the vendor description further down refers to a different platform or
> a different model variant and does not apply here.

## Use this model from an AI agent

Atlas Cloud ships three first-party integration surfaces. All three authenticate
with the same API key via the `ATLASCLOUD_API_KEY` environment variable.

### MCP server

The official MCP server (`atlascloud-mcp`) exposes this model to any
MCP-compatible host — Claude Code, OpenAI Codex, Cursor, Gemini CLI, Goose,
Claude Desktop. One-line install:

```bash
# Claude Code
claude mcp add atlascloud -- npx -y atlascloud-mcp

# OpenAI Codex CLI
codex mcp add atlascloud -- npx -y atlascloud-mcp

# Gemini CLI
gemini mcp add atlascloud -- npx -y atlascloud-mcp

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

Then ask in plain English; the agent calls `atlas_generate_image` with `model: "atlascloud/photo-cleanup"`.
The server fetches each model's schema and validates parameters before submitting,
so invalid requests fail fast without spending credits.

MCP docs: https://www.atlascloud.ai/docs/mcp-server

### Agent Skills

`atlas-cloud-skills` is a portable skill package (API reference, code templates in
Python / Node.js / cURL, model IDs with pricing) for Claude Code, Cursor, Codex and
12+ other agents:

```bash
npx skills add AtlasCloudAI/atlas-cloud-skills
export ATLASCLOUD_API_KEY="your-api-key"
```

Skills docs: https://www.atlascloud.ai/docs/skills

### CLI

The `atlas` binary runs Atlas Cloud from a terminal or CI script. Async media jobs
are polled and downloaded automatically (use `--no-download` when a script only
needs the output URLs):

```bash
# Install (Homebrew, npm, or shell installer)
brew install AtlasCloudAI/tap/atlascloud
# npm install -g atlascloud-cli
# curl -fsSL https://raw.githubusercontent.com/AtlasCloudAI/cli/main/install.sh | sh

atlas auth login
atlas generate image atlascloud/photo-cleanup -p "Your prompt here"
```

CLI docs: https://www.atlascloud.ai/docs/cli

## HTTP API reference

- **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**: `atlascloud/photo-cleanup`


## 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: `"atlascloud/photo-cleanup"`
  - Options: "atlascloud/photo-cleanup"

- **`image`** (`string`, _required_):
  Input image URL to clean up.

- **`output_format`** (`string`, _optional_):
  Output image format.
  - Default: `"png"`
  - Options: "png", "jpeg", "webp", "jpg"



**Required Parameters Example**:

```json
{
  "model": "atlascloud/photo-cleanup",
  "image": ""
}
```


**Full Example**:

```json
{
  "model": "atlascloud/photo-cleanup",
  "image": "",
  "output_format": "png"
}
```


### 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 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/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "atlascloud/photo-cleanup",
  "image": "",
  "output_format": "png"
}'

# 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/atlascloud/photo-cleanup)

## About this model

_Vendor-supplied description. Any pricing or endpoint mentioned below refers to_
_other platforms — use the Atlas Cloud values above._

### Photo Cleanup

Photo Cleanup restores single images by reducing dust, scratches, noise, and repeated micro-texture artifacts. It is designed for post-processing generated or uploaded images that need a cleaner surface without changing the image content.

#### Highlights

- **Image cleanup chain:** Runs dust cleanup followed by denoise cleanup for a balanced restoration pass.
- **Dust and scratch reduction:** Softens small specks, scratch-like marks, and dirty surface artifacts.
- **Noise and micro-texture reduction:** Reduces repeated fine texture, scale-like artifacts, and noisy high-frequency edges.
- **Content-preserving output:** Keeps the original composition, subject, framing, and color intent.
- **Single-image workflow:** Optimized for still images. Video cleanup is not supported by this model.

#### Parameters

| Parameter | Required | Description |
|-----------|----------|-------------|
| `model` | Yes | Use `atlascloud/photo-cleanup`. |
| `image` | Yes | Input image URL. |
| `output_format` | No | Output image format: `png`, `jpeg`, `webp`, or `jpg`. Default: `png`. |

#### Limits

Photo Cleanup is an image-to-image model. It does not upscale images, does not process video input, and does not expose the internal single-stage cleanup runners as public parameters.

Use input images that are accessible by URL. Very large inputs may be rejected by the backend before processing.

#### Example Request

```json
{
  "model": "atlascloud/photo-cleanup",
  "image": "https://atlas-test-input.oss-us-west-1.aliyuncs.com/1024_square.png",
  "output_format": "png"
}
```

#### Best For

- Cleaning generated images with visible repeated micro-texture or scale-like artifacts.
- Reducing dust, fine scratches, and small specks in still images.
- Softening noisy water, metal, foliage, glass, or reflective texture regions.
- Preparing images for visual review when the source image is already close to usable.

#### Pricing

Photo Cleanup uses flat per-image billing.

_(Description truncated. Full text on the model page.)_

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Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/atlascloud/photo-cleanup · Docs: https://www.atlascloud.ai/docs