Ai Travel Trends
Playground
Try it on WavespeedAI!AI Travel Trends generates stunning travel-style photos at 30 iconic destinations worldwide. Upload a photo, write a prompt, pick a destination — Paris, Tokyo, Bali, New York, and more. Ready-to-use REST inference API, no coldstarts, affordable pricing.
Features
AI Travel Trends
AI Travel Trends teleports you to 30 iconic destinations around the world — without leaving your chair. Upload a photo, describe your dream shot, pick a destination, and AI places you there instantly.
Why Choose This?
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30 iconic destinations From the Eiffel Tower to Machu Picchu — choose your dream location or let the model surprise you with a random pick.
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Prompt-driven scene control Describe the lighting, mood, and composition you want for a more personalized travel photo.
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Instant results No travel, no photoshoot, no editing skills required. Professional-quality output in seconds.
Parameters
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the desired travel scene and photo style. |
| image | Yes | Input photo (URL or file upload). |
| destination | No | Destination preset. Default: auto (random). See options below. |
Destination Options
Europe paris, santorini, swiss_alps, iceland, rome, london, amalfi, barcelona, norway_fjords
Asia tokyo, kyoto, bali, maldives, dubai, great_wall, taj_mahal, angkor_wat, seoul
Americas new_york, machu_picchu, havana, grand_canyon, rio
Africa & Middle East safari, marrakech, pyramids, capetown
Oceania sydney, new_zealand
Special auto — random destination (default)
How to Use
- Upload your photo — a clear, well-lit photo works best.
- Write a prompt — describe the travel scene, lighting, and mood you want.
- Pick a destination — choose from 30 locations, or leave as auto for a surprise.
- Submit — download your travel photo.
Pricing
Just $0.05 per image.
Best Use Cases
- Social media content — Create stunning travel photos for Instagram and TikTok without the flights.
- Personal branding — Place yourself in aspirational locations for a globetrotter aesthetic.
- Creative exploration — Try the same photo across multiple destinations for a travel series.
- Gifts & fun — Surprise friends with a photo of them at their dream destination.
Pro Tips
- Be specific in your prompt — describe time of day, weather, and composition for more accurate results.
- Try the same photo across several destinations to find your favorite look.
- Use auto mode when you want a creative surprise.
- Clear, front-facing photos with a visible subject produce the most convincing placements.
Notes
- Both prompt and image are required fields.
- Destination defaults to auto (random) if not specified.
- Ensure image URLs are publicly accessible if using a link rather than a direct upload.
- Please ensure your content complies with WaveSpeed AI’s usage policies.
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/wavespeed-ai/ai-travel-trends" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"destination": "auto"
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| image | string | Yes | - | The URL of the input image. | |
| prompt | string | No | - | Text prompt describing the desired travel photo. | |
| destination | string | No | auto | auto, paris, santorini, swiss_alps, iceland, rome, london, amalfi, barcelona, norway_fjords, tokyo, kyoto, bali, maldives, dubai, great_wall, taj_mahal, angkor_wat, seoul, new_york, machu_picchu, havana, grand_canyon, rio, safari, marrakech, pyramids, capetown, sydney, new_zealand | Destination preset for the travel photo. 'auto' picks a random destination. |
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | string | Array of URLs to the generated content (empty when status is not completed). |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |