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Stable Diffusion 3

by Stability AI· Released June 2024

Stable Diffusion 3 is a state-of-the-art text-to-image model that excels in generating high-quality, photorealistic images with improved typography and complex prompt understanding. It builds on the success of its predecessors with a new diffusion transformer architecture and is available in multiple sizes (800M to 8B parameters) to suit different use cases.

Official Site API Docs 🤗 Hugging Face 📄 Research Paper

Input cost

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Output cost

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Context window

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Max output

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Modalities

image

Parameters

License

Stability AI Community License

Capabilities

Text-to-Image GenerationImage-to-Image GenerationInpaintingOutpaintingHigh Resolution (up to 1024x1024)Improved TypographyMulti-Subject Composition

Best For

Generating high-quality, photorealistic images from detailed text prompts with accurate text rendering.

Strengths

Excellent typography and text rendering in images
Superior prompt adherence and complex scene composition
High photorealism and image quality
Available in multiple sizes for flexibility
Open-source weights available for research

Limitations

Requires significant computational resources for larger models
May still produce artifacts in highly complex scenes
Not as fast as smaller, distilled models
Limited to 1024x1024 resolution natively
Potential for biased or unsafe outputs if not filtered

Use Cases

Generating marketing visuals and advertisements

Creating concept art and illustrations

Designing product mockups with text

Enhancing creative workflows for designers

Educational content creation

Rapid prototyping of visual ideas

Artistic exploration and experimentation

Improvements Over Previous Model

New diffusion transformer architecture (MMDiT) for better text-image alignment
Significantly improved typography and text rendering compared to SDXL
Better understanding of complex prompts with multiple subjects
Higher image quality and photorealism
Available in multiple parameter sizes (800M to 8B) for scalability
Improved efficiency with rectified flow sampling

Back to all models

Improvements Over Previous Model

New diffusion transformer architecture (MMDiT) for better text-image alignment

Significantly improved typography and text rendering compared to SDXL

Better understanding of complex prompts with multiple subjects

Higher image quality and photorealism

Available in multiple parameter sizes (800M to 8B) for scalability

Improved efficiency with rectified flow sampling