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About the Provider

Pruna AI is an AI optimization company focused on making state-of-the-art image generation fast, affordable, and accessible. Their P-Image model family is designed to deliver professional-quality image generation and editing with sub-second inference speeds, strong prompt adherence, and support for fine-tuning and LoRA-based customization.

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Model Quickstart

This section helps you quickly get started with the p-image-edit model on the Qubrid AI inferencing platform. To use this model, you need:
  • A valid Qubrid API key
  • Access to the Qubrid inference API
  • Basic knowledge of making API requests in your preferred language
Once authenticated with your API key, you can send inference requests to the p-image-edit model and receive responses based on your input prompts. Below are example placeholders showing how the model can be accessed using different programming environments.
You can choose the one that best fits your workflow. 
import requests
import json

url = "https://platform.qubrid.com/v1/images/edits"
headers = {
    "Authorization": "Bearer QUBRID_API_KEY"
}

# ─────────────────────────────────────────
# Option 1: File Upload (Multipart)
# ─────────────────────────────────────────
files = [
    ('files', ('input.png', open('<YOUR-IMAGE-COMPLETE-PATH>', 'rb'), 'image/png'))
]
data = {
  "model": "p-image-edit",
  "prompt": "Add a rainbow over the mountains",
  "aspect_ratio": "16:9",
  "seed": 1,
  "disable_safety_checker": False,
  "response_format": "url"
}

response = requests.post(url, headers=headers, data=data, files=files)
print(response.json())

# ─────────────────────────────────────────
# Option 2: JSON with Image URLs
# ─────────────────────────────────────────
headers_json = {
    "Authorization": "Bearer QUBRID_API_KEY",
    "Content-Type": "application/json"
}
payload = {
  "model": "p-image-edit",
  "prompt": "Add a rainbow over the mountains",
  "aspect_ratio": "16:9",
  "seed": 1,
  "disable_safety_checker": False,
  "response_format": "url"
}
payload["images"] = ["<YOUR-IMAGE-URL-1>", "<YOUR-IMAGE-URL-2>"]

response = requests.post(url, headers=headers_json, json=payload)
print(response.json())

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API Generated Response

  • Input Image
Edit Prompt: Add a rainbow over the mountains
  • Output Image

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Model Overview

P-Image Edit is an image editing variant of the P-Image model developed by Pruna AI, designed for precise text-guided modifications to existing images.
  • It accepts both file uploads and image URLs as input, providing flexibility for different integration workflows.
  • The model supports text-guided edits while preserving the overall visual structure and fidelity of the original image.
  • P-Image Edit is suitable for production workflows requiring fast, scalable, and high-quality image editing capabilities.

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Model at a Glance

FeatureDetails
Model IDp-image-edit
ProviderPruna AI
Model TypeText-Guided Image Editing
ArchitectureOptimized latent diffusion model with image conditioning
Input TypeImage file upload or image URL
Output TypeEdited image based on text prompt

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When to use?

You should consider using P-Image Edit if:
  • You need text-guided editing of existing images with high visual fidelity
  • Your application requires flexible input support via file upload or image URLs
  • You want fast, scalable image editing without full model retraining
  • You need precise prompt-driven modifications while preserving the original image structure

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Inference Parameters

Parameter NameTypeDefaultDescription
Promptstring—Text instruction describing the desired edit.
Aspect Ratiostring16:9Output image aspect ratio.
Seednumber1Random seed for reproducibility.
Disable Safety CheckerbooleanfalseDisables the built-in safety filter if enabled.
Response FormatstringurlFormat of the response — url or b64_json.

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Key Features

  • Text-Guided Image Editing: Performs precise modifications to existing images based on natural language instructions while preserving visual fidelity.
  • Flexible Input Support: Accepts both multipart file uploads and JSON-based image URLs for seamless integration into diverse workflows.
  • High Visual Fidelity: Maintains the structural integrity and visual quality of the original image during the editing process.
  • Sub-Second Inference: Delivers fast image editing results optimized for production-scale workloads.
  • Safety Controls: Built-in safety checker with optional override for appropriate use cases.

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Summary

P-Image Edit is a fast, precise text-guided image editing model built by Pruna AI for production-scale workflows.
  • It supports both file upload and URL-based image inputs for flexible integration.
  • The model performs accurate prompt-driven edits while maintaining the visual structure of the original image.
  • It is suitable for developers and enterprises requiring scalable, high-quality image editing capabilities.