Image Generation

Overview

Atlas Cloud provides access to a wide range of AI image generation models through a unified API. Generate stunning images from text prompts, transform existing images, remove backgrounds, swap faces, and more — all with a single API call.

Supported Model Types

Type	Description	Use Cases
Text-to-Image	Generate images from text descriptions	Creative content, marketing, design, prototyping
Image-to-Image	Transform and enhance existing images	Style transfer, inpainting, outpainting
Image Tools	Advanced image processing and manipulation	Background removal, face swap, upscaling, restoration

Featured Models

Model	Provider	Highlights
Seedream	ByteDance	High-quality text-to-image with excellent prompt following
FLUX	Black Forest Labs	Fast, high-fidelity image generation with multiple variants (Dev, Schnell, Pro)
Qwen-Image	Alibaba	Powerful multilingual image generation
Ideogram	Ideogram	Excellent text rendering in images
HiDream	HiDream	Creative and artistic image generation
Nano Banana	Google	Fast, high-quality image generation

For a complete list of all image models and their specifications, visit the Model Library.

API Usage

Text-to-Image

import requests

response = requests.post(
    "https://api.atlascloud.ai/api/v1/model/generateImage",
    headers={
        "Authorization": "Bearer your-api-key",
        "Content-Type": "application/json"
    },
    json={
        "model": "seedream-3.0",
        "prompt": "A serene Japanese garden with cherry blossoms, watercolor style"
    }
)

result = response.json()
prediction_id = result["data"]["id"]
print(f"Prediction ID: {prediction_id}")

Node.js Example

const response = await fetch(
  "https://api.atlascloud.ai/api/v1/model/generateImage",
  {
    method: "POST",
    headers: {
      Authorization: "Bearer your-api-key",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "seedream-3.0",
      prompt:
        "A serene Japanese garden with cherry blossoms, watercolor style",
    }),
  }
);

const predictionId = (await response.json()).data.id;
console.log(`Prediction ID: ${predictionId}`);

cURL Example

curl -X POST https://api.atlascloud.ai/api/v1/model/generateImage \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "seedream-3.0",
    "prompt": "A serene Japanese garden with cherry blossoms, watercolor style"
  }'

Get Image Result

Image generation is asynchronous. Use the prediction ID to retrieve the result:

import requests
import time

def get_image_result(prediction_id, api_key):
    while True:
        response = requests.get(
            f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}",
            headers={"Authorization": f"Bearer {api_key}"}
        )
        result = response.json()

        if result["data"]["status"] == "completed":
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(f"Generation failed: {result['data'].get('error')}")

        time.sleep(2)  # Poll every 2 seconds

image_url = get_image_result(prediction_id, "your-api-key")
print(f"Image URL: {image_url}")

Using LoRA Models

You can enhance image generation with LoRA (Low-Rank Adaptation) models for custom styles and fine-grained control. See the LoRA Guide for detailed instructions on finding, selecting, and using LoRA models with Atlas Cloud.

Tips for Better Results

Be specific: Describe style, composition, lighting, and mood in your prompt
Use negative prompts: Specify what you don't want in the image (if supported by the model)
Experiment with models: Different models excel at different styles — photorealistic, anime, artistic, etc.
Adjust parameters: Each model has unique parameters. Check the model's detail page on the Model Library for available options
Use seed values: Set a seed for reproducible results when iterating on prompts

Industry-Leading Speed

Atlas Cloud's optimized inference infrastructure delivers fast image generation speeds — under 5 seconds for most models. Combined with competitive pricing, it's ideal for both prototyping and production workloads.

For the full API specification and model-specific parameters, see the API Reference.

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