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Documentation Index

Fetch the complete documentation index at: https://docs.together.ai/llms.txt

Use this file to discover all available pages before exploring further.

Using a coding agent? Install the together-images skill to let your agent write correct image generation code automatically. See agent skills for details.

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Generate an image

To query an image model, use the .images method and specify the image model: 
from together import Together

client = Together()

# Generate an image from a text prompt
response = client.images.generate(
    prompt="A serene mountain landscape at sunset with a lake reflection",
    model="black-forest-labs/FLUX.1-schnell",
    steps=4,
)

print(f"Image URL: {response.data[0].url}")
Example response structure and output: 
{
  "id": "oFuwv7Y-2kFHot-99170ebf9e84e0ce-SJC",
  "model": "black-forest-labs/FLUX.1-schnell",
  "data": [
    {
      "index": 0,
      "url": "https://api.together.ai/v1/images/..."
    }
  ]
}
Generated image of a serene mountain landscape at sunset with a lake reflection.

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Supported models

For the current list of image-generation models, see the serverless catalog or the dedicated endpoint model catalog.

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Parameters

The image generation endpoint accepts a common set of parameters across models, including prompt, model, width, height, n, steps, seed, and negative_prompt. For the full parameter reference (including image_url, reference_images, frame_images, and model-specific notes), see Image generation parameters. A few quick notes:
  • prompt is required for all models except Kling.
  • width and height rely on defaults unless otherwise specified. Available dimensions differ by model.
  • FLUX Schnell and Kontext (Pro/Max/Dev) models use the aspect_ratio parameter to set the output image size, whereas FLUX.1 Pro, FLUX 1.1 Pro, and FLUX.1 Dev use width and height.

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Generate multiple variations

Generate multiple variations of the same prompt and choose between them: 
response = client.images.generate(
    prompt="A cute robot assistant helping in a modern office",
    model="black-forest-labs/FLUX.1-schnell",
    n=4,
    steps=4,
)

print(f"Generated {len(response.data)} variations")
for i, image in enumerate(response.data):
    print(f"Variation {i+1}: {image.url}")
Example output: Multiple generated image variations

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Next steps