AI Image Generation: DALL-E, Stable Diffusion, & More

Transform your text descriptions into stunning visuals with our powerful Image Generation API. Perfect for developers, designers, and businesses looking to automate image creation, generate marketing assets, or build creative applications. Our API supports multiple leading AI models and offers enterprise-grade reliability with detailed usage analytics.

🎨 Image Generation Overview

The Image Generation API allows developers to create images from textual descriptions using state-of-the-art AI models. The API is fully compliant with OpenAI's API structure, enabling easy integration and migration from existing OpenAI-based applications.

Open AI compatible framework

Simply change the URL and your API key, and your application built on the OpenAI API can start using APIpie immediately. Once integrated, you can leverage all our additional features and capabilities.

🎯 Model Comparison and Capabilities

The AI image generation landscape is rapidly evolving, with new models and capabilities being released regularly. Visit our models route for the most up-to-date list of supported image models and their capabilities. Here's a current comparison of major models:

Feature	DALL-E 3	DALL-E 2	Stable Diffusion XL	Stable Diffusion 2.1	Flux	Amazon Titan
Style Control	✅	❌	✅	✅	✅	✅
Image Editing	❌	✅	✅	✅	✅	✅
Photorealistic Quality	High	Medium	High	Medium-High	High	High
Processing Speed	Fast	Very Fast	Medium	Fast	Fast	Fast
Cost	Higher	Medium	Lower	Lower	Medium	Medium
Customization	Limited	Limited	Extensive	Extensive	Moderate	Moderate

Model Strengths and Use Cases

DALL-E 3 (OpenAI Documentation)

Photorealistic scenes and complex compositions
Accurate text rendering in images (Research Paper)
Detailed human faces and expressions
Architectural visualization

Explore DALL-E 3's advanced capabilities in OpenAI's Comprehensive Guide to DALL-E 3

DALL-E 2 (Model Overview)

Quick iterations and variations
Simple product images
Abstract art and patterns
Basic image editing and variations

Understand DALL-E 2's features and limitations in the Official DALL-E 2 System Card

Stable Diffusion Models (Stability AI)

SDXL: Enhanced photorealism and composition
SD 2.1: Balanced performance and quality (GitHub Repository)
Custom Models: Community-trained variations (Hugging Face Hub)
Strengths:
- Extensive customization options
- Fine-tuning capabilities (Guide)
- Model merging and custom training
- Strong artistic style control
- Open-source foundation (License)

Flux (Official Documentation)

Multiple model variants:
- Pro models (FLUX.1-pro, FLUX.1.1-pro) for highest quality
- Specialized models for depth and edge-guided generation
- Performance models (schnell series) for faster results
Enterprise-grade reliability
Extensive customization options
Optimized for production use

Explore Flux's technical details and implementation guides in the Official Flux Model Repository

Amazon Titan (AWS Documentation)

Enterprise-grade reliability
Consistent output quality
Good for production workloads

Discover AWS Titan's enterprise capabilities in the AWS Titan Image Generation Guide

Model Landscape and Evolution

The image generation field is rapidly advancing, with new models and capabilities being released frequently. Key trends include:

Increasing Quality
- Higher resolution outputs
- Better photorealism
- Improved text rendering
- More accurate human features
Enhanced Control
- Better style consistency
- More precise prompting
- Advanced editing capabilities
- Fine-grained parameter control
Customization Options
- Model fine-tuning
- Custom training
- Style adaptation
- Domain specialization
Performance Improvements
- Faster generation times
- More efficient resource usage
- Better scaling capabilities
- Reduced costs

For the most current information about available models and their capabilities, always refer to our models documentation. We regularly update our supported models to include the latest advancements in the field.

🔄 How Image Generation Works

A typical API call sends a text prompt describing the desired image, along with optional parameters to control the generation process. The API then processes this input and returns either a URL to the generated image or the image data directly as a base64-encoded string, depending on your specified response format.

Example API Call

Below is an example of how to use the Image Generation API to create an image:

curl -L -X POST 'https://apipie.ai/v1/images/generations' \
-H 'Content-Type: application/json' \
-H 'Accept: application/json' \
-H 'Authorization: Bearer <YOUR_API_KEY>' \
--data-raw '{
  "provider": "openai",
  "model": "dall-e-3",
  "prompt": "A serene lake at sunset with mountains in the background",
  "n": 1,
  "size": "1024x1024",
  "quality": "standard",
  "style": "natural"
}'

Response Example

The expected response structure looks like the following:

{
  "created": 1729535643,
  "data": [
    {
      "url": "https://apipie.ai/temp/47g43g6f476vf.jpg",
      "revised_prompt": "A tranquil mountain lake reflecting the warm colors of sunset, with majestic peaks silhouetted against a golden sky, creating a peaceful and atmospheric natural scene"
    }
  ]
}

📝 API Parameters and Configuration

Required Parameters

prompt (string): A text description of the desired image. Be specific and descriptive for best results.
model (string): Specifies the AI model to use for image generation (e.g., "dall-e-3", "dall-e-2", "stable-diffusion-3").

Optional Parameters

provider (string): Optionally specify the AI provider, or omit this field to let the system choose the best-performing one.
n (integer): Number of images to generate. Currently only supports generating 1 image at a time (n=1).
size (string): The size of the generated image. Available options:
- "1024x1024" (default) - Supported by all models
- "1024x1792" (portrait)
- "1792x1024" (landscape)
- "512x512" (legacy)
- "256x256" (legacy)
quality (string): The quality of the generated image. Options:
- "standard" (default)
- "hd" (higher quality, may increase latency)
style (string): The stylistic approach for image generation. Options:
- "natural" (default)
- "vivid" (more vibrant and dramatic)
response_format (string): The format of the response. Options:
- "url" (default): Returns a URL to the generated image
- "b64_json": Returns the image as a base64-encoded string
image (string): Optional URL to an image you want the image model to update/modify. Note: Not supported by DALL-E 3.

Example API Call with Optional Parameters

curl -L -X POST 'https://apipie.ai/v1/images/generations' \
-H 'Content-Type: application/json' \
-H 'Accept: application/json' \
-H 'Authorization: Bearer <YOUR_API_KEY>' \
--data-raw '{
  "provider": "openai",
  "model": "dall-e-3",
  "prompt": "A futuristic cityscape at night with flying cars and neon lights",
  "n": 1,
  "size": "1024x1024",
  "quality": "hd",
  "style": "vivid",
  "response_format": "b64_json"
}'

Response with Base64 Format

{
  "created": 1729535643,
  "data": [
    {
      "b64_json": "iVBORw0KGgoAAAANSUhEUgAA...",
      "revised_prompt": "A sprawling futuristic metropolis illuminated by vibrant neon signs and streams of flying vehicles weaving between towering skyscrapers, creating a dynamic and energetic nighttime cityscape"
    }
  ]
}

💡 Common Use Cases

Our Image Generation API supports a wide range of applications:

E-commerce Product Visualization
- Generate product images from descriptions
- Create lifestyle shots for marketing
- Visualize custom product variations
Content Creation
- Generate blog post illustrations
- Create social media visuals
- Design marketing materials
Game Development
- Generate concept art
- Create texture assets
- Design character variations
Educational Content
- Illustrate complex concepts
- Create educational diagrams
- Generate visual examples

⚡ Usage Metrics and Analytics

We provide comprehensive usage data with every request to help you track costs and performance. Metrics include:

cost: The estimated cost of the request.
latency_ms: Time taken for the image generation.
image_size: Size of the generated image(s) in pixels.
model_used: The specific model version used for generation.

Example Response with Usage Metrics

{
  "created": 1729535643,
  "data": [
    {
      "url": "https://apipie.ai/temp/47g43g6f476vf.jpg",
      "revised_prompt": "A serene lake at sunset with mountains in the background"
    }
  ],
  "usage": {
    "cost": 0.04,
    "latency_ms": 4521,
    "image_size": "1024x1024",
    "model_used": "dall-e-3"
  }
}

Note: We provide detailed usage tracking for every request. Historical billing data is available via API for audit purposes.

🎓 Best Practices for Prompts

Understanding Model Strengths and Specialties

Different models excel at different types of images and use cases:

DALL-E 3

Best for photorealistic images and complex scenes
Excellent at following detailed instructions
Strong at maintaining text accuracy in images
Ideal for commercial and professional use

DALL-E 2

Excellent for artistic interpretations
Quick for rapid prototyping
Good for simple compositions
Efficient for basic image variations

Stable Diffusion Models

SDXL: Superior composition and detail
SD 2.1: Excellent style control
Custom Models: Specialized for specific domains
Great for artistic and creative projects

Flux

Pro variants: Best for high-quality commercial work
Schnell variants: Ideal for rapid prototyping
Specialized variants: Perfect for specific tasks (depth, edges)
Development variants: Great for testing and integration

Amazon Titan

Reliable for production environments
Strong at maintaining brand guidelines
Good for batch processing
Consistent quality across variations

Model-Specific Prompt Tips

DALL-E 3

Be very specific about details
Use clear, structured descriptions
Specify camera angles and lighting
Include technical photography terms

Stable Diffusion

Use artistic style keywords
Include composition keywords
Reference specific artists or movements
Experiment with negative prompts

Flux

Balance descriptive and technical terms
Use clear style references
Include mood and atmosphere details
Specify brand guidelines when relevant

Amazon Titan

Use professional terminology
Include technical specifications
Be precise about brand requirements
Structure prompts systematically

Advanced Prompt Techniques

To get the best results from the Image Generation API, consider these prompt engineering tips:

Be Specific and Detailed
- Include style, lighting, perspective, and mood
- Specify camera details for photographic results
- Mention color schemes and textures
Use Clear, Structured Language
- Avoid ambiguous descriptions
- Structure prompts from general to specific
- Use artistic terminology when relevant
Consider Composition Elements
- Describe foreground and background
- Specify focal points and subject placement
- Include depth and perspective information
Reference Artistic Styles
- Name specific art movements or artists
- Describe technical aspects (brush strokes, medium)
- Include time period references if relevant
Follow Best Practices
- Ensure prompts comply with content guidelines
- Test and iterate on prompts
- Save successful prompt patterns

⚠️ Error Handling and Troubleshooting

Common errors and their solutions:

Rate Limiting

{
  "error": {
    "code": "rate_limit_exceeded",
    "message": "You have exceeded your rate limit. Please try again later."
  }
}

Solution: Implement exponential backoff in your requests.

Invalid Parameters

{
  "error": {
    "code": "invalid_parameter",
    "message": "The 'size' parameter must be one of: 1024x1024, 1024x1792, 1792x1024"
  }
}

Solution: Verify parameter values against the documentation.

Content Policy Violations

{
  "error": {
    "code": "content_policy_violation",
    "message": "Your request was rejected as it violates our content policy."
  }
}

Solution: Review and adjust your prompt to comply with content guidelines.

Performance Optimization Tips

Implement exponential backoff for rate limits
Use async requests for batch processing
Monitor and optimize request patterns
Consider using different models based on speed requirements

Quality Improvement Tips

Try increasing the quality parameter to "hd"
Use more specific descriptions in your prompt
Consider using a different model better suited to your use case
Save successful prompts as templates
Break complex scenes into simpler components

🎨 Image Generation Overview​

Open AI compatible framework​

🎯 Model Comparison and Capabilities​

Model Strengths and Use Cases​

Model Landscape and Evolution​

🔄 How Image Generation Works​

Example API Call​

Response Example​

📝 API Parameters and Configuration​

Required Parameters​

Optional Parameters​

Example API Call with Optional Parameters​

Response with Base64 Format​

💡 Common Use Cases​

⚡ Usage Metrics and Analytics​

Example Response with Usage Metrics​

🎓 Best Practices for Prompts​

Understanding Model Strengths and Specialties​

Model-Specific Prompt Tips​

Advanced Prompt Techniques​

⚠️ Error Handling and Troubleshooting​

Rate Limiting​

Invalid Parameters​

Content Policy Violations​

Performance Optimization Tips​

Quality Improvement Tips​