Public endpoints

RunPod public endpoints provide instant access to state-of-the-art AI models through simple API calls, with an API playground available through the RunPod Hub.

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Available models

Our initial launch includes optimized text-to-image generation models:

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Public endpoint playground

The public endpoint playground provides a streamlined way to discover and experiment with AI models.

The playground offers:

• Interactive parameter adjustment: Modify prompts, dimensions, and model settings in real-time.
• Instant preview: Generate images directly in the browser.
• Cost estimation: See estimated costs before running generation.
• API code generation: Create working code examples for your applications.

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Access the playground

1. Navigate to the RunPod Hub in the console.
2. Select the Public endpoints section.
3. Browse the available models and select one that fits your needs.

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Test a model

To test a model in the playground:

1. Select a model from the Runpod Hub.
2. Under Input, enter a prompt in the text box.
3. Enter a negative prompt if needed. Negative prompts tell the model what to exclude from the output.
4. Under Additional settings, you can adjust the seed, aspect ratio, number of inference steps, guidance scale, and output format.
5. Click Run to start generating.

Under Result, you can use the dropdown menu to show either a preview of the output, or the raw JSON.

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Create a code example

After inputting parameters using the playground, you can automatically generate an API request to use in your application.

1. Select the API tab in the UI (above the Input field).
2. Using the dropdown menu, select the programming language (Python, JavaScript, cURL, etc.) and POST command you want to use (/run or /runsync).
3. Click the Copy icon to copy the code to your clipboard.

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Make API requests to public endpoints

You can make API requests to public endpoints using any HTTP client. The endpoint URL is specific to the model you want to use.

All requests require authentication using your RunPod API key, passed in the Authorization header. You can find and create API keys in the RunPod console under Settings > API Keys.

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Synchronous request example

Here’s an example of a synchronous request to Flux Dev using the /runsync endpoint:

curl -X POST "https://api.runpod.ai/v2/black-forest-labs-flux-1-dev/runsync" \
  -H "Authorization: Bearer RUNPOD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "prompt": "A serene mountain landscape at sunset",
      "width": 1024,
      "height": 1024,
      "num_inference_steps": 20,
      "guidance": 7.5
    }
  }'

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Asynchronous request example

Here’s an example of an asynchronous request to Flux Dev using the /run endpoint:

curl -X POST "https://api.runpod.ai/v2/black-forest-labs-flux-1-dev/run" \
  -H "Authorization: Bearer RUNPOD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "prompt": "A futuristic cityscape with flying cars",
      "width": 1024,
      "height": 1024,
      "num_inference_steps": 50,
      "guidance": 8.0
    }
  }'

You can check the status and retrieve results using the /status endpoint, replacing {job-id} with the job ID returned from the /run request:

curl -X GET "https://api.runpod.ai/v2/black-forest-labs-flux-1-dev/status/{job-id}" \
  -H "Authorization: Bearer RUNPOD_API_KEY"

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Response format

All endpoints return a consistent JSON response format:

{
{
  "delayTime": 17,
  "executionTime": 3986,
  "id": "sync-0965434e-ff63-4a1c-a9f9-5b705f66e176-u2",
  "output": {
    "cost": 0.02097152,
    "image_url": "https://image.runpod.ai/6/6/mCwUZlep6S/453ad7b7-67c6-43a1-8348-3ad3428ef97a.png"
  },
  "status": "COMPLETED",
  "workerId": "oqk7ao1uomckye"
}

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Model-specific parameters

Each endpoint accepts a different set of parameters to control the generation process.

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Flux Dev

Flux Dev is optimized for high-quality, detailed image generation. The model accepts several parameters to control the generation process:

{
  "input": {
    "prompt": "A serene mountain landscape at sunset",
    "negative_prompt": "Snow",
    "width": 1024,
    "height": 1024,
    "num_inference_steps": 20,
    "guidance": 7.5,
    "seed": 42,
    "image_format": "png"
  }
}

Parameters:

• prompt (string, required): Text description of the desired image.
• negative_prompt (string, optional): Elements to exclude from the image.
• width (integer, default: 1024): Image width in pixels (256-1536).
• height (integer, default: 1024): Image height in pixels (256-1536).
• num_inference_steps (integer, default: 28): Number of denoising steps (1-50).
• guidance (float, default: 7.5): How closely to follow the prompt (0.0-10.0).
• seed (integer, optional, default: -1): Provide a seed for reproducible results. The default value (-1) will generate a random seed.
• image_format (string, default: “jpeg”): Output format (“png” or “jpeg”).

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Flux Schnell

Flux Schnell is optimized for speed and real-time applications:

{
  "input": {
    "prompt": "A quick sketch of a mountain",
    "width": 1024,
    "height": 1024,
    "num_inference_steps": 4,
    "guidance": 1.0,
    "seed": 123
  }
}

Parameters:

• prompt (string, required): Text description of the desired image.
• negative_prompt (string, optional): Elements to exclude from the image.
• width (integer, default: 1024): Image width in pixels (256-1536).
• height (integer, default: 1024): Image height in pixels (256-1536).
• num_inference_steps (integer, default: 4): Number of denoising steps (1-8).
• guidance (float, default: 7.5): How closely to follow the prompt (0.0-10.0).
• seed (integer, optional, default: -1): Provide a seed for reproducible results. The default value (-1) will generate a random seed.

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Python example

Here is an example Python API request to Flux Dev using the /run endpoint:

import requests

headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer RUNPOD_API_KEY'
}

data = {
    'input': {"prompt":"A serene mountain landscape at sunset","image_format":"png","num_inference_steps":25,"guidance":7,"seed":50,"width":1024,"height":1024}
}

response = requests.post('https://api.runpod.ai/v2/black-forest-labs-flux-1-dev/run', headers=headers, json=data)

You can generate public endpoint API requests for Python and other programming languages using the public endpoint playground.

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Pricing

Public endpoints use transparent, usage-based pricing:

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Pricing examples

Below are some pricing examples that show how you can estimate costs for different image sizes:

• 512×512 image (0.25 megapixels)

  • Flux Dev: (512 * 512 / 1,000,000) * $0.02 = $0.00524288
  • Flux Schnell: (512 * 512 / 1,000,000) * $0.0024 = $0.0006291456

• 1024×1024 image (1 megapixel)

  • Flux Dev: (1024 * 1024 / 1,000,000) * $0.02 = $0.02097152
  • Flux Schnell: (1024 * 1024 / 1,000,000) * $0.0024 = $0.0025165824

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Best practices

When working with public endpoints, following best practices will help you achieve better results and optimize performance.

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Prompt engineering

For prompt engineering, be specific with detailed prompts as they generally produce better results. Include style modifiers such as art styles, camera angles, or lighting conditions. For Flux Dev, use negative prompts to exclude unwanted elements from your images.

A good prompt example would be: “A professional portrait of a woman in business attire, studio lighting, high quality, detailed, corporate headshot style.”

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Performance optimization

For performance optimization, choose the right model for your needs. Use Flux Schnell when you need speed, and Flux Dev when you need higher quality. Standard dimensions like 1024×1024 render fastest, so stick to these unless you need specific aspect ratios. For multiple images, use asynchronous endpoints to batch your requests. Consider caching results by storing generated images to avoid regenerating identical prompts.