stabilityai/stable-diffusion-3.5-large
This model generates and edits images from text prompts using a Latent Diffusion framework, with dual CLIP text encoders.
api_example.sh
Technical Specifications
Model Architecture & Performance
Pricing
Pay-per-use, no commitments
API Reference
Complete parameter documentation
| Parameter | Type | Default | Description |
|---|---|---|---|
| width | number | 1024 | Image width in pixels |
| height | number | 1024 | Image height in pixels |
| steps | number | 30 | Number of denoising steps |
| cfg | number | 7.5 | How closely to follow the prompt |
| seed | number | 50 | Random seed for reproducibility |
| negative_prompt | string | What to exclude from the image. | |
| response_format | string | url | Format of the generated image response. Options: url (default), base64. |
Explore the full request and response schema in our external API documentation
Resources
Learn, watch, and build faster
Performance
Strengths & considerations
| Strengths | Considerations |
|---|---|
| Excellent prompt following High quality image generation Good text rendering in images | Still requires prompt engineering High-resolution complex scenes may need more compute |
Use cases
Recommended applications for this model
Enterprise
Platform Integration
Docker Support
Official Docker images for containerized deployments
Kubernetes Ready
Production-grade KBS manifests and Helm charts
SDK Libraries
Official SDKs for Python, Javascript, Go, and Java
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"Qubrid's medical OCR and research parsing cut our document extraction time in half. We now have traceable pipelines and reproducible outputs that meet our compliance requirements."
Clinical AI Team
Research & Clinical Intelligence
