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 is developed by Ideogram. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.
Your request will cost $0.008 per run. For $10 you can run this model approximately 1250 times.
Here's what you can do next:
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()Install the required package for your language.
pip install requestsAll 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"import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.
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 an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.
/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"
}
}Poll the prediction endpoint to check the current status of your request.
/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)processingThe request is still being processed.completedGeneration is complete. Outputs are available.succeededGeneration succeeded. Outputs are available.failedGeneration failed. Check the error field.{
"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 to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.
/api/v1/model/uploadMediaimport requests
url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }
with open("image.png", "rb") as f:
files = {"file": ("image.png", f, "image/png")}
response = requests.post(url, headers=headers, files=files)
result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}"){
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}The following parameters are accepted in the request body.
{
"model": "ideogram/v4/turbo/text-to-image",
"prompt": "A beautiful landscape",
"image_size": "landscape_16_9",
"output_format": "jpeg"
}The API returns a prediction response with the generated output URLs.
{
"id": "pred_abc123",
"status": "completed",
"model": "model-name",
"outputs": [
"https://storage.atlascloud.ai/outputs/result.png"
],
"metrics": {
"predict_time": 8.3
},
"created_at": "2025-01-01T00:00:00Z",
"completed_at": "2025-01-01T00:00:10Z"
}Atlas Cloud Skills integrates 400+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.
npx skills add AtlasCloudAI/atlas-cloud-skillsGet your API key from the Atlas Cloud dashboard and set it as an environment variable.
export ATLASCLOUD_API_KEY="your-api-key-here"Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.
Atlas Cloud MCP Server connects your IDE with 400+ AI models via the Model Context Protocol. Works with any MCP-compatible client.
npx -y atlascloud-mcpAdd the following configuration to your IDE's MCP settings file.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}{
"info": {
"description": "The AtlasCloud API.",
"title": "AtlasCloud API",
"version": "1.0.0"
},
"paths": {
"/api/v1/model/generateImage": {
"post": {
"responses": {
"200": {
"content": {
"application/json": {
"schema": {
"$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.