AI Image Generation Models - A Performance Analysis

The landscape of AI image generation has evolved rapidly, with multiple open-source models now offering varying trade-offs between quality, speed, and resource requirements. This comprehensive analysis examines the performance characteristics and optimal configurations of leading models, based on extensive testing conducted using the ImageGenAI.

Whether you’re building real-time applications, creating professional content, or working with limited computational resources, this guide will help you choose the right model for your specific needs.

Key Findings

Here are the best results achieved with each model at their optimal settings:

SDXL - stabilityai/stable-diffusion-xl-base-1.0 SDXL Turbo - stabilityai/sdxl-turbo PixArt - PixArt-alpha/PixArt-LCM
SDXL SDXL Turbo PixArt
Highest detail and photorealism, ideal for professional work (11-50 seconds generation) Excellent quality with remarkable speed (1-2 seconds generation) Artistic style with efficient performance (3 seconds generation)
Steps: 50 Steps: 4 Steps: 4
Guidance: 7.5 Guidance: 2.0 Guidance: 2.0
Size: 1024x1024 Size: 768x768 Size: 768x768
Time: 48seconds Time: 1-2seconds Time: 3seconds

Test Environment

All the performance metrics were measured on an AWS g4dn.xlarge instance with the following specifications:

GPU                                   CPU     RAM    
NVIDIA T4 Tensor Core GPU (16GB VRAM) 4 vCPUs 16 GiB

All reported generation times and memory usage metrics are based on this hardware configuration

Methodology

Our analysis focused on three key metrics: image quality, generation speed, and resource utilization. Each model was tested using identical prompts across multiple runs to ensure consistent benchmarking. Quality assessments combine both objective metrics and subjective evaluation of image coherence and detail.

Model Characteristics

Model Optimized For Best Resolution Generation Time Key Feature
SDXL Quality 1024x1024 48s Professional results
SDXL Turbo Speed 768x768 1-2s Real-time generation
PixArt-α Balance 768x768 3s Artistic quality
RunwayML CPU Use 512x512 10s No GPU required

Optimal Parameters

Model Steps Guidance Scale Use Case
SDXL 50 7.5 High-detail professional work
SDXL Turbo 4 2.0 Rapid prototyping, real-time
PixArt-α 4 1.0-2.0 General purpose, artistic
RunwayML 50 7.5 Development, testing

Model Trade-off Analysis

The following visualizations demonstrate the key performance trade-offs between quality, speed, and resource requirements - three critical factors in choosing the right model for your use case:

Speed vs Quality Trade-offs

The following chart shows the relationship between generation time and output quality for each model:

flowchart LR
    SDXL["SDXL
    Quality: Highest
    Speed: 48s"]
    SDXLT["SDXL Turbo
    Quality: Good
    Speed: 2s"]
    PixArt["PixArt-α
    Quality: Very Good
    Speed: 3s"]
    RunwayML["RunwayML
    Quality: Basic
    Speed: 10s"]

    SDXL --> Professional[Professional Use]
    SDXLT --> RealTime[Real-time Apps]
    PixArt --> Balanced[Balanced Use]
    RunwayML --> Development[Development]

Model Performance Summary:

  • SDXL: Optimized for quality, best for professional work but slowest
  • SDXL Turbo: Optimized for speed, excellent for real-time with good quality
  • PixArt-α: Balanced performance, suitable for most use cases with high quality
  • RunwayML: CPU-capable, suitable for development, moderate speed and quality

Resource Requirements

Memory Usage Distribution (16GB VRAM)

pie title "GPU Memory Usage (at max resolution)"
  "SDXL" : 14
  "SDXL Turbo" : 8
  "PixArt-α" : 10

Performance Characteristics

  1. Quality Comparison (1-10 scale) 
    %%{init: {'theme': 'default'}}%%
graph LR
    subgraph Quality Score
        SDXL["SDXL (10/10)"]
        SDXLT["SDXL Turbo (7/10)"]
        PixArt["PixArt-α (8/10)"]
        RunwayML["RunwayML (5/10)"]
    end
    style SDXL fill:#90EE90
    style SDXLT fill:#B8E6B8
    style PixArt fill:#A8E2A8
    style RunwayML fill:#D8F0D8
  2. Speed Comparison (Generation Time) 
    %%{init: {'theme': 'default'}}%%
graph LR
    subgraph Generation Speed
        SDXLT2["SDXL Turbo (2s)"]
        PixArt2["PixArt-α (3s)"]
        RunwayML2["RunwayML (10s)"]
        SDXL2["SDXL (48s)"]
    end
    style SDXLT2 fill:#90EE90
    style PixArt2 fill:#B8E6B8
    style RunwayML2 fill:#D8F0D8
    style SDXL2 fill:#F0F8F0
  3. Memory Usage (GB VRAM) 
    %%{init: {'theme': 'default'}}%%
graph LR
    subgraph VRAM Usage
        RunwayML3["RunwayML (4GB)"]
        SDXLT3["SDXL Turbo (8GB)"]
        PixArt3["PixArt-α (10GB)"]
        SDXL3["SDXL (14GB)"]
    end
    style RunwayML3 fill:#90EE90
    style SDXLT3 fill:#B8E6B8
    style PixArt3 fill:#D8F0D8
    style SDXL3 fill:#F0F8F0

Darker green indicates better performance in each category

Use Case Scenarios

  • Content Creation Platforms: SDXL - Where consistent, high-quality outputs justify longer generation times
  • Interactive Applications: SDXL Turbo - When sub-second generation is crucial for user experience
  • Creative Tools: PixArt-α - For applications requiring a balance of quality and responsiveness
  • Development/Testing: RunwayML - When working with limited computing resources

Conclusion

Each model offers distinct advantages in a local deployment context:

  • SDXL: Best for professional quality when time isn’t critical
  • SDXL Turbo: Ideal for rapid prototyping and real-time applications
  • PixArt-α: Excellent balance of speed and quality, especially for artistic outputs
  • SD v1.4/RunwayML: Perfect for CPU deployment and development environments

The choice of model and parameters should be based on your specific needs:

  • For real-time applications: SDXL Turbo
  • For highest quality: SDXL (requires GPU)
  • For balanced performance: PixArt-α (requires GPU)
  • For CPU-only systems: RunwayML v1.5 (lower quality but no GPU required)

References & Tools

This analysis was conducted using ImageGenAI, an open-source project that provides:

  • A flexible, containerized API server supporting multiple AI image generation models
  • Local deployment capabilities for complete data control and cost management
  • Independence from cloud service providers
  • Comprehensive benchmarking tools for performance analysis

For detailed implementation and deployment information, visit: