P Image Edit LoRA | Custom LoRA Image API

Pruna AI P-Image Edit LoRA

Pruna AI P-Image Edit LoRA edits one or more input images using a natural-language instruction, with optional LoRA guidance for stronger style or edit control. It is designed for workflows where you want prompt-based image editing together with a LoRA trained specifically for the Pruna p-image-edit-lora pipeline.

Why Choose This?

LoRA-guided image editing Edit images with natural-language instructions while steering the result with a compatible LoRA.
Multi-image reference support Use one to five input images to guide appearance, structure, composition, or scene transformation.
Edit-specific LoRA control Apply lora_weights and tune lora_scale for stronger stylistic or transformation control.
Flexible aspect ratio handling Use match_input_image to follow the first input image by default, or select a preset aspect ratio when needed.
Private LoRA support Use hf_api_token when accessing a private or gated Hugging Face LoRA repository.
Simple fixed pricing Each run uses a flat per-image price.

Parameters

Parameter	Required	Description
prompt	Yes	Text instruction describing the desired edit.
images	Yes	One to five reference images used for the edit. When using multiple images, describe their roles clearly in the prompt.
lora_weights	No	Optional Hugging Face LoRA path, such as `huggingface.co/PrunaAI/p-image-edit-next-scene-lora/weights.safetensors`. The LoRA should be trained for p-image-edit-lora.
lora_scale	No	LoRA strength. Default: `0.5`. Official range: `-1` to `3`.
hf_api_token	No	Optional Hugging Face token for private or gated LoRA repositories.
aspect_ratio	No	Output aspect ratio. Default: `match_input_image`, which follows the first input image. Other supported values: `1:1`, `16:9`, `9:16`, `4:3`, `3:4`, `3:2`, and `2:3`.
output_format	No	Output image format: `png`, `jpeg`, or `webp`.
seed	No	Random seed. Use `-1` for random generation.

How to Use

Upload your reference images — provide one to five images you want to use for the edit.
Write your prompt — describe what should change and what should remain unchanged.
Add a LoRA (optional) — provide lora_weights if you want LoRA-guided editing.
Adjust LoRA strength (optional) — set lora_scale to control how strongly the LoRA affects the result.
Choose aspect ratio — keep match_input_image to follow the first input image, or select a preset ratio if needed.
Add a Hugging Face token (optional) — use hf_api_token if your LoRA is private or gated.
Choose output format — select png, jpeg, or webp.
Set a seed (optional) — use -1 for random output, or a fixed value for more reproducible edits.
Submit — run the model and download the edited image.

Example Prompt

Make this scene look like the next scene style.

Pricing

Just $0.01 per generated image.

Best Use Cases

Scene-to-scene style transfer — Rework one scene to match the visual style of another.
Character-consistent editing — Preserve identity while changing mood, styling, or composition.
Reference-guided transformations — Use multiple images and a LoRA for tighter edit control.
Creative adaptation — Turn existing images into new variants for storytelling, design, or campaign work.
Edit-specific LoRA workflows — Apply LoRAs trained for editing rather than text-to-image generation.

Pro Tips

Use a LoRA trained specifically for p-image-edit-lora for best compatibility.
Text-to-image LoRAs should be used with p-image-lora, not this model.
When using multiple images, explain each image’s role clearly in the prompt.
Use match_input_image when you want to preserve the framing of the first input image.
Adjust lora_scale gradually to balance prompt influence and LoRA influence.
If your LoRA is private or gated on Hugging Face, provide hf_api_token.
Reuse the same seed when you want more consistent edit iterations.

Notes

Both prompt and images are required.
images supports one to five input images.
lora_weights is optional.
LoRAs for this model should be trained for p-image-edit-lora.
Text-to-image LoRAs should be used with p-image-lora instead.
aspect_ratio defaults to match_input_image, which follows the first input image.
seed uses -1 for random generation.
The backend sends turbo=false and disables the safety checker by default in the internal mapping; these are not user-facing controls.
Pricing is fixed at $0.01 per generated image.

Related Models

Pruna AI P-Image Text-to-Image — Generate images directly from prompts.
Pruna AI P-Image Text-to-Image LoRA — Generate images from prompts with LoRA style control.
Pruna AI P-Image Edit — Edit images with natural-language instructions without a LoRA.
Pruna AI P-Image Upscale — Enhance and enlarge existing images.

P Image Edit Lora API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-image/edit-lora with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for P Image Edit Lora below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-image/edit-lora" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "lora_scale": 1,
    "aspect_ratio": "match_input_image",
    "output_format": "png",
    "seed": -1,
    "enable_sync_mode": false,
    "enable_base64_output": false
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("pruna-ai/p-image/edit-lora", {
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "lora_scale": 1,
        "aspect_ratio": "match_input_image",
        "output_format": "png",
        "seed": -1,
        "enable_sync_mode": false,
        "enable_base64_output": false
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "pruna-ai/p-image/edit-lora",
    {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "lora_scale": 1,
    "aspect_ratio": "match_input_image",
    "output_format": "png",
    "seed": -1,
    "enable_sync_mode": false,
    "enable_base64_output": false
}
)

print(output["outputs"][0])  # → URL of the generated output

P Image Edit Lora API — Frequently asked questions

What is the P Image Edit Lora API?

P Image Edit Lora is a Pruna Ai model for AI inference, exposed as a REST API on WaveSpeedAI. Pruna AI P-Image Edit LORA is a fast AI image editing model that edits and transforms images with LORA-based customization. Ready-to-use REST inference API for text-guided image editing, style changes, character consistency, product image updates, marketing assets, and custom AI editing workflows with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the P Image Edit Lora API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/pruna-ai/pruna-ai-p-image-edit-lora.

How much does P Image Edit Lora cost per run?

P Image Edit Lora starts at $0.010 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does P Image Edit Lora accept?

Key inputs: `prompt`, `images`, `aspect_ratio`, `seed`, `enable_base64_output`, `enable_sync_mode`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/pruna-ai/pruna-ai-p-image-edit-lora.

How do I get started with the P Image Edit Lora API?

Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.

Can I use P Image Edit Lora outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Pruna Ai). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

ExamplesView all

Related Models

README

Pruna AI P-Image Edit LoRA

Why Choose This?

Parameters

How to Use

Example Prompt

Pricing

Best Use Cases

Pro Tips

Notes

Related Models

P Image Edit Lora API — Quick start

P Image Edit Lora API — Frequently asked questions