Generate high-quality images, posters, and logos with Ideogram latest V4.0 — producing crisp visuals with accurate text rendering, fine detail, and full creative control for polished, ready-to-use designs.
Ideogram v4 Turbo Text-to-Image は Ideogram が開発したモデルです。Atlas Cloud（運営：Atlas Cloud AI LLC）は本モデルへのアクセスを提供するものであり、その所有者ではありません。すべての商標は各所有者に帰属します。
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": "ideogram/v4/turbo/text-to-image", # Required. model name
"prompt": "A beautiful landscape with mountains and lake", # Required. Text description of the image to generate
"image_size": "landscape_16_9", # Output image size preset. options: square_hd | square | portrait_3_4 | portrait_9_16 | landscape_4_3 | landscape_16_9
"output_format": "jpeg", # File format of the generated image. options: jpeg | png
"seed": 0, # Random seed for reproducibility. (min: 0)
}
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()お使いの言語に必要なパッケージをインストールしてください。
pip install requestsすべての API リクエストには API キーによる認証が必要です。API キーは Atlas Cloud ダッシュボードから取得できます。
export ATLASCLOUD_API_KEY="your-api-key-here"import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}API キーをクライアントサイドのコードや公開リポジトリに公開しないでください。代わりに環境変数またはバックエンドプロキシを使用してください。
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())非同期生成リクエストを送信します。API は予測 ID を返し、それを使用してステータスの確認や結果の取得ができます。
/api/v1/model/generateImageimport requests
url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "ideogram/v4/turbo/text-to-image",
"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']}"){
"code": 200,
"data": {
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}
}予測エンドポイントをポーリングして、リクエストの現在のステータスを確認します。
/api/v1/model/prediction/{prediction_id}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)processingリクエストはまだ処理中です。completed生成が完了しました。出力が利用可能です。succeeded生成が成功しました。出力が利用可能です。failed生成に失敗しました。エラーフィールドを確認してください。{
"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"
}
}Atlas Cloud ストレージにファイルをアップロードし、API リクエストで使用できる URL を取得します。multipart/form-data を使用してアップロードします。
/api/v1/model/uploadMediaimport requests
url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }
with open("image.png", "rb") as f:
files = {"file": ("image.png", f, "image/png")}
response = requests.post(url, headers=headers, files=files)
result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}"){
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}以下のパラメータがリクエストボディで使用できます。
{
"model": "ideogram/v4/turbo/text-to-image",
"prompt": "A beautiful landscape",
"image_size": "landscape_16_9",
"output_format": "jpeg"
}API は生成された出力 URL を含む予測レスポンスを返します。
{
"id": "pred_abc123",
"status": "completed",
"model": "model-name",
"outputs": [
"https://storage.atlascloud.ai/outputs/result.png"
],
"metrics": {
"predict_time": 8.3
},
"created_at": "2025-01-01T00:00:00Z",
"completed_at": "2025-01-01T00:00:10Z"
}Atlas Cloud Skills は 400 以上の AI モデルを AI コーディングアシスタントに直接統合します。ワンコマンドでインストールし、自然言語で画像・動画生成や LLM との対話が可能です。
npx skills add AtlasCloudAI/atlas-cloud-skillsAtlas Cloud ダッシュボードから API キーを取得し、環境変数として設定してください。
export ATLASCLOUD_API_KEY="your-api-key-here"インストール後、AI アシスタントで自然言語を使用してすべての Atlas Cloud モデルにアクセスできます。
Atlas Cloud MCP Server は Model Context Protocol を通じて IDE と 400 以上の AI モデルを接続します。MCP 対応のあらゆるクライアントで動作します。
npx -y atlascloud-mcp以下の設定を IDE の MCP 設定ファイルに追加してください。
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}{
"info": {
"description": "The AtlasCloud API.",
"title": "AtlasCloud API",
"version": "1.0.0"
},
"paths": {
"/api/v1/model/generateImage": {
"post": {
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$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": "ideogram/v4/turbo/text-to-image"
},
"prompt": {
"description": "Text description of the image to generate. Maximum 2,000 characters.",
"type": "string",
"minLength": 1,
"maxLength": 2000
},
"image_size": {
"description": "Output image size preset.",
"type": "string",
"x-atlas": {
"max_width": 3840,
"min_height": 512,
"min_width": 512,
"max_height": 3840,
"multiple_of": 16
},
"default": "landscape_16_9",
"enum": [
"square_hd",
"square",
"portrait_3_4",
"portrait_9_16",
"landscape_4_3",
"landscape_16_9"
],
"x-ui-component": "select",
"x-enum-labels": {
"square_hd": "Square HD (1024×1024)",
"square": "Square (512×512)",
"portrait_3_4": "Portrait 3:4",
"portrait_9_16": "Portrait 9:16",
"landscape_4_3": "Landscape 4:3",
"landscape_16_9": "Landscape 16:9"
}
},
"output_format": {
"description": "File format of the generated image.",
"type": "string",
"default": "jpeg",
"enum": [
"jpeg",
"png"
],
"x-ui-component": "select"
},
"seed": {
"description": "Random seed for reproducibility. Use the same seed and prompt to reproduce results.",
"type": "integer",
"minimum": 0
}
},
"required": [
"model",
"prompt"
],
"type": "object",
"x-order-properties": [
"model",
"prompt",
"image_size",
"output_format",
"seed"
]
},
"PredictionResponse": {
"properties": {
"model": {
"type": "string",
"description": "model name",
"default": "ideogram/v4/turbo/text-to-image"
},
"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 images. Null when status is not completed.",
"items": {
"type": "string"
},
"nullable": true,
"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"
}
}
}
}# ideogram/v4/turbo/text-to-image
> Generate high-quality images, posters, and logos with Ideogram latest V4.0 — producing crisp visuals with accurate text rendering, fine detail, and full creative control for polished, ready-to-use designs.
## 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**: `ideogram/v4/turbo/text-to-image`
## 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: `"ideogram/v4/turbo/text-to-image"`
- **`prompt`** (`string`, _required_):
Text description of the image to generate. Maximum 2,000 characters.
- **`image_size`** (`string`, _optional_):
Output image size preset.
- Default: `"landscape_16_9"`
- Options: "square_hd", "square", "portrait_3_4", "portrait_9_16", "landscape_4_3", "landscape_16_9"
- **`output_format`** (`string`, _optional_):
File format of the generated image.
- Default: `"jpeg"`
- Options: "jpeg", "png"
- **`seed`** (`integer`, _optional_):
Random seed for reproducibility. Use the same seed and prompt to reproduce results.
- Min: 0
**Required Parameters Example**:
```json
{
"model": "ideogram/v4/turbo/text-to-image",
"prompt": ""
}
```
**Full Example**:
```json
{
"model": "ideogram/v4/turbo/text-to-image",
"prompt": "",
"image_size": "landscape_16_9",
"output_format": "jpeg",
"seed": 0
}
```
### Output Schema
The API returns the following output format:
- **`model`** (`string`, _optional_):
model name
- Default: `"ideogram/v4/turbo/text-to-image"`
- **`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 images. Null 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
{
"model": "ideogram/v4/turbo/text-to-image",
"created_at": "",
"id": "",
"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": "ideogram/v4/turbo/text-to-image",
"prompt": "",
"image_size": "landscape_16_9",
"output_format": "jpeg",
"seed": 0
}'
# 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/ideogram/v4/turbo/text-to-image)
Endless tea terraces stretch across mist-covered mountains at dawn. Soft sunlight illuminates emerald-green rows while low clouds drift gracefully between the hills. Peaceful cinematic landscape, ultra realistic.
Endless tea terraces stretch across mist-covered mountains at dawn. Soft sunlight illuminates emerald-green rows while low clouds drift gracefully between the hills. Peaceful cinematic landscape, ultra realistic.
Endless lavender fields flow across rolling hills beneath a dramatic evening sky. A narrow path winds through the flowers toward a distant stone cottage. Romantic countryside, cinematic landscape.
Endless lavender fields flow across rolling hills beneath a dramatic evening sky. A narrow path winds through the flowers toward a distant stone cottage. Romantic countryside, cinematic landscape.