Luma Photon Modify
Playground
Try it on WaveSpeedAI!Luma Photon Modify is a text-to-image generation model that converts text prompts into images for creative and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Features
Edit and transform images with natural language using Luma Photon Modify. Simply describe the changes you want — swap backgrounds, modify elements, or transform scenes — and watch your image transform instantly. Perfect for quick edits, background replacements, and creative modifications.
Why It Looks Great
- Natural language editing: Describe changes in plain English — no complex tools needed.
- Background replacement: Seamlessly swap backgrounds while preserving subjects.
- Scene transformation: Modify environments, lighting, and context effortlessly.
- Subject preservation: Maintains the integrity of your main subject during edits.
- Prompt Enhancer: Built-in tool to refine your editing instructions.
- Ultra-affordable: Professional image editing at just $0.015 per image.
Parameters
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text instruction describing the edit or modification you want. |
| image | No | Source image to modify (upload or public URL). |
How to Use
- Write your edit instruction — describe what you want to change.
- Use Prompt Enhancer (optional) — click to refine your instruction.
- Upload your image — drag and drop or paste a public URL.
- Run — click the button to apply the edit.
- Download — preview and save your modified image.
Pricing
Flat rate per image.
| Output | Cost |
|---|---|
| Per image | $0.015 |
Best Use Cases
- Background Replacement — Change backgrounds to any scene or setting.
- Scene Transformation — Move subjects to different environments or contexts.
- Creative Compositing — Place subjects in imaginative or fantastical settings.
- Product Photography — Swap product backgrounds for professional presentations.
- Social Media Content — Quick edits for engaging visual content.
Example Prompts
- “Change the background to large performance stage”
- “Replace the background with a tropical beach at sunset”
- “Put the subject in a futuristic cityscape”
- “Change the setting to a cozy coffee shop interior”
- “Transform the background to a snowy mountain landscape”
- “Place in a professional studio with white backdrop”
Pro Tips for Best Results
- Be specific about the new background or setting you want.
- Photon Modify excels at background replacements and scene changes.
- Include lighting and atmosphere details for more cohesive results.
- Works best when the subject is clearly defined in the original image.
- Use for quick iterations — at $0.015, you can experiment freely.
- Combine with descriptive atmosphere: “dramatic lighting”, “warm tones”, “soft focus”.
Notes
- If using a URL, ensure it is publicly accessible.
- Best results come from images with clear subject-background separation.
- Processing is fast and efficient for rapid workflow integration.
- Ideal for batch processing due to low cost per image.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
set -euo pipefail
export WAVESPEED_API_KEY="your-api-key"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic ocean wave at sunrise, highly detailed"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/luma/photon-modify" \
-H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
-H "Content-Type: application/json" \
-d "${REQUEST_BODY}")
TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "${TASK}" | jq -r '.urls.get // empty')
if [ -z "${RESULT_URL}" ]; then RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"; fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body \
"${RESULT_URL}" \
-H "Authorization: Bearer ${WAVESPEED_API_KEY}")
RESULT=$(printf '%s' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')
case "${STATUS}" in
completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "${STATUS}" >&2; exit 1 ;;
esac
doneParameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| prompt | string | Yes | - | The positive prompt for the generation. | |
| image | string | No | - | The image to generate an image from. | |
| enable_base64_output | boolean | No | false | - | If set to `true`, the prediction's `output` strings are returned as **naked base64** (no `data:<mime>;base64,` prefix). When `false` (default), outputs are returned as URLs pointing to our CDN. |
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 | Output values, usually URL strings; some models return text strings or structured result objects (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 |
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 |
| data.model | string | Model ID used for the prediction |
| data.outputs | array<string | object> | Array of generated outputs (empty when status is not completed). Items are usually URL strings, but may be text strings or structured result objects, depending on the model. |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to poll for the prediction result |
| data.status | string | Status: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created |
| 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 |