WaveSpeedAI APILeonardoaiLeonardoai Phoenix 1.0

Leonardoai Phoenix 1.0

Leonardoai Phoenix 1.0

Playground

Try it on WavespeedAI!

Phoenix by Leonardo.Ai delivers strong prompt fidelity, legible in-image text, and iterative edits via Edit with AI for text-to-image. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Leonardo Phoenix 1.0

Flagship text-to-image model from Leonardo. Phoenix focuses on prompt fidelity, clean in-image text, and iterative editing so you can move from sketch to final artwork without jumping tools.

Why it shines

  • Exceptional prompt adherence — Follows long, multi-constraint prompts with high fidelity; complex scene layouts stay consistent.
  • Coherent text in images — Renders readable logos, labels, posters, and UI mockups with far fewer misspellings.
  • Iterative “Edit with AI” — Inline refinements without starting over: add/remove objects, restyle, or adjust composition.
  • High resolution — Supports outputs up to ~5MP (e.g., 2048×2048 and beyond) for print-friendly, zoomable detail.
  • Cinematic realism or stylized art — Handles filmic lighting, texture, and depth as well as illustration and graphic design.

Price

Just $0.038 for each image!!!

How to use

  1. Write a precise prompt: subject, setting, camera/POV, materials, lighting, mood, color palette.
  2. Choose aspect_ratio to match destination (e.g., 4:5 for feeds, 9:16 for stories, 16:9 for slides).
  3. Generate → Edit with AI to add/remove elements, restyle, or fix text without losing composition.
  4. Lock a seed when you like a look; iterate for variants with controlled diversity.

Prompt patterns (copy & adapt)

  • Product hero (text): “Matte black wireless earbuds on acrylic stand, soft top light + rim light, centered product label ‘AURIS PRO’, shallow DOF, studio backdrop, clean reflections, minimal luxury aesthetic.”
  • Cinematic portrait: “Soft morning window light, medium close-up, 85mm look, skin texture natural, pastel color palette, bokeh background, film grain subtle.”
  • Brand style carry-over (I2I): “Preserve layout and typography from reference; restyle to retro-futurism, chrome accents, teal-magenta grade.”

Common use cases

  • Brand comps and posters
  • Packaging and labels
  • UI/landing mockups
  • Editorial/stock art
  • Key visuals for ads
  • Story beats/boards

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/leonardoai/phoenix-1.0" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "aspect_ratio": "16:9",
    "enable_sync_mode": false,
    "enable_base64_output": false
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
promptstringYes-The positive prompt for the generation.
aspect_ratiostringNo16:91:1, 16:9, 9:16, 3:2, 2:3, 4:5, 5:4, 3:4, 4:3, 2:1, 1:2, 3:1, 1:3The aspect ratio of the generated media.
enable_sync_modebooleanNofalse-If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.
enable_base64_outputbooleanNofalse-If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

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