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Core.Today
|
Stability AIMediumHigh

Stable Diffusion XL

Industry-standard open-source image generation model. Highly versatile with excellent community support and fine-tuning options.

8 credits
per image
Open-source foundation
Extensive fine-tuned variants
Strong community ecosystem
LoRA/embedding support
Proven reliability

Quick Start

curl -X POST "https://api.core.today/v1/predictions" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_API_KEY" \
  -d '{
  "model": "stable-diffusion-xl",
  "input": {
    "prompt": "Majestic dragon perched on ancient castle ruins, dramatic sunset, epic fantasy art, detailed scales, glowing eyes, masterpiece quality",
    "negative_prompt": "blurry, low quality, distorted",
    "width": 1024,
    "height": 1024,
    "num_inference_steps": 30,
    "guidance_scale": 7.5
  }
}'

Parameters

ParameterTypeRequiredDefaultDescription
promptstringYes-Text description of the image to generate
negative_promptstringNo-Things to exclude from the image
widthintegerNo1024Image width (512-1536)
heightintegerNo1024Image height (512-1536)
num_inference_stepsintegerNo30Number of denoising steps
guidance_scalefloatNo7.5Prompt adherence strength (1-20)
seedintegerNo-Random seed for reproducibility

Examples

Fantasy Art

Create fantasy illustrations

curl -X POST "https://api.core.today/v1/predictions" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_API_KEY" \
  -d '{
  "model": "stable-diffusion-xl",
  "input": {
    "prompt": "Majestic dragon perched on ancient castle ruins, dramatic sunset, epic fantasy art, detailed scales, glowing eyes, masterpiece quality",
    "negative_prompt": "blurry, low quality, distorted",
    "width": 1024,
    "height": 1024,
    "num_inference_steps": 30,
    "guidance_scale": 7.5
  }
}'

Tips & Best Practices

1Use negative prompts to exclude unwanted elements
2Standard guidance_scale of 7.5 works well for most cases
3Combine with specific artist styles for unique results
4Works well with detailed, structured prompts

Use Cases

General-purpose image generation
Art style experimentation
Custom model fine-tuning
Production workloads
Style-specific generations