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Bria Fibo Reseason

Bria Fibo Reseason

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Bria Reseason changes the season or weather atmosphere of an image. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

Features

Bria Fibo Reseason

Bria Fibo Reseason is an AI-powered image transformation model that changes the season of any outdoor scene. Upload a landscape or outdoor photo and select a target season — the model intelligently transforms foliage, lighting, and atmosphere to match your chosen time of year.


Why Choose This?

  • Four season options Transform images to spring, summer, autumn, or winter.

  • Intelligent scene understanding AI recognizes trees, foliage, sky, and ground to apply realistic seasonal changes.

  • Natural transformations Adjusts colors, lighting, and atmosphere for believable results.

  • Simple workflow Just upload an image and select a season — instant transformation.

  • Versatile applications Perfect for real estate, marketing, and creative projects.


Parameters

ParameterRequiredDescription
imageYesOutdoor/landscape image to transform (URL or upload)
seasonYesTarget season: spring, summer, autumn, winter

Season Options

SeasonDescription
springFresh green foliage, blooming flowers, bright atmosphere
summerLush green leaves, warm lighting, vibrant colors
autumnGolden and red foliage, warm tones, falling leaves
winterSnow coverage, bare trees, cold blue tones

How to Use

  1. Upload your image — provide an outdoor or landscape photo.
  2. Select season — choose the target season you want.
  3. Run — submit and download your transformed image.

Pricing

OutputCost
Per image$0.04

Best Use Cases

  • Real Estate Marketing — Show properties in their best seasonal light.
  • Travel & Tourism — Display destinations across different seasons.
  • Creative Projects — Explore seasonal variations of landscapes.
  • Marketing Campaigns — Create season-specific promotional materials.
  • Film & Video — Pre-visualize scenes in different seasons.

Pro Tips

  • Works best with outdoor scenes featuring trees, landscapes, or natural elements.
  • Winter transformation adds snow — ideal for creating holiday-themed content.
  • Autumn transformation creates warm, golden tones perfect for cozy aesthetics.
  • Use high-quality source images for more realistic seasonal transformations.
  • Combine with Bria Fibo Relight to also adjust the lighting for maximum impact.

Notes

  • Both image and season are required fields.
  • Works best on outdoor scenes with visible vegetation or landscapes.
  • Indoor scenes or close-up portraits may not transform as expected.
  • Ensure uploaded image URLs are publicly accessible.

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/bria/fibo/reseason" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "season": "spring"
}'

# 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
imagestringYes-The source image to change season.
seasonstringYesspringspring, summer, autumn, winterThe desired season to apply.

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

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsstringArray of URLs to the generated content.
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
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
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