Pruna Ai P Image Edit
Playground
Try it on WavespeedAI!Pruna AI P-Image Edit is a fast AI image editing model that edits and transforms images based on text instructions and reference images. Ready-to-use REST inference API for photo retouching, creative edits, product image updates, background changes, marketing assets, and AI image editing workflows with simple integration, no coldstarts, and affordable pricing.
Features
Pruna AI P-Image Edit
Pruna AI P-Image Edit transforms one or more reference images using natural-language editing instructions. It is designed for image modification workflows such as outfit changes, style transfer, object replacement, visual refinement, and other prompt-guided image edits.
Why Choose This?
-
Prompt-driven image editing Edit images by describing exactly what you want to change in natural language.
-
Multi-image reference support Upload one or more images to guide the edit, making it easier to preserve identity, style, or visual consistency.
-
Flexible aspect ratio control Choose the output aspect ratio that best fits your use case.
-
Multiple output formats Export the edited image in a supported format such as
png. -
Seed support for reproducibility Use
seedto get more consistent results across repeated edits. -
Simple fixed pricing Each edit run uses a straightforward per-image price.
Parameters
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text instruction describing the desired edit. |
| images | Yes | One or more reference images used for the edit. |
| aspect_ratio | No | Output aspect ratio for the edited image. |
| output_format | No | Output image format, such as png. |
| seed | No | Random seed for reproducibility. Use the same seed to get more consistent results. |
How to Use
- Upload your reference images — provide one or more images you want to use for the edit.
- Write your prompt — describe what should change and what should stay consistent.
- Choose aspect ratio (optional) — select the output ratio that matches your target use case.
- Choose output format (optional) — select the format that best fits your workflow.
- Set a seed (optional) — use a fixed seed for more reproducible results.
- Submit — run the model and download the edited image.
Example Prompt
Change the figure 1 man’s suit to the clothes in figure 2.
Pricing
Just $0.01 per image.
Best Use Cases
- Outfit and apparel changes — Replace clothing or accessories while preserving the subject.
- Style transfer — Apply the look or styling of one image to another.
- Visual refinement — Improve or adjust details using prompt-based instructions.
- Character consistency edits — Modify appearance while keeping identity and composition stable.
- Creative image adaptation — Rework an existing image into a new variation for marketing, design, or social content.
- Reference-guided editing — Use multiple images when you need stronger control over the desired result.
Pro Tips
- Be specific about what should change and what should remain unchanged.
- If using multiple images, make sure each image clearly supports the edit you want.
- Mention identity, clothing, background, or composition explicitly when consistency matters.
- Use the same
seedwhen you want to iterate on an edit with more consistent outputs. - Keep prompts short and direct for simple edits, and add more detail only when necessary.
Notes
- Both
promptandimagesare required. imagescan include one or more reference images.seedhelps with reproducibility but may not guarantee identical results in every case.- Pricing is fixed at $0.01 per image.
Related Models
- Pruna AI P-Image Text-to-Image — Generate new images directly from natural-language prompts.
- Other Pruna AI image generation and image editing models may be useful when you need different quality, speed, or workflow trade-offs.
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/pruna-ai/p-image/edit" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"aspect_ratio": "match_input_image",
"output_format": "png",
"seed": -1,
"enable_sync_mode": false,
"enable_base64_output": false
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| prompt | string | Yes | - | Edit instruction. | |
| images | array | Yes | [] | 1 ~ 5 items | Reference image URLs. Upload 1 to 5 images. |
| aspect_ratio | string | No | match_input_image | match_input_image, 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3 | Aspect ratio of the generated image. |
| output_format | string | No | png | png, jpeg, webp | Output image format. |
| seed | integer | No | -1 | -1 ~ 2147483647 | Random seed. -1 means random. |
| enable_sync_mode | boolean | No | false | - | 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_output | boolean | No | false | - | 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
| 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.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. |
| 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 |