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Object Eraser API Pricing

Easily remove unwanted objects, people, or text from any image using AI. Just select the area you want to erase, and the model will intelligently fill the space with realistic background matching the surrounding environment. No Photoshop skills needed.

Savings Alert38% ↓Cheaper than Fal.ai

About Object Eraser

The ai-object-eraser is a cutting-edge AI tool designed to seamlessly remove unwanted objects, people, or text from images. Utilizing advanced image processing techniques and deep learning algorithms, this model identifies the area to be removed with high precision and fills it with a realistic background. This offers a quick and efficient way to edit images without requiring any specialized software like Photoshop. Built for both casual users and professionals, the model leverages state-of-the-art convolutional neural networks to ensure that the erased region blends perfectly with the surrounding environment. Its user-friendly interface and impressive automation capabilities make it a go-to choice for tasks ranging from social media image editing to professional graphic design, all while keeping processing costs low at just $0.05 per generation.

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Estimate monthly API spend and compare absolute developer savings.

Monthly API Generations10,000 runs
50025,00050,00075,000100,000+
MuAPI Monthly Cost

$500.00

$0.05 per generation
Fal.ai Cost

$800.00

$0.08 per generation
Estimated Monthly Savings$300.00
Annual Savings$3600.00

Detailed Pricing Breakdown

ProviderEstimated RateNotes
muapiapp$0.05 per generationOur pricing is 20-50% more affordable than competitors, delivering comparable or superior quality at a lower cost.
Fal.ai$0.08 per generationFal.ai charges a higher rate, making muapiapp a more budget-friendly choice while maintaining high standards of output.
Replicate$0.08 per generationReplicate's pricing is similar to Fal.ai, meaning muapiapp offers a significant cost advantage, being 20-50% cheaper without compromising on quality.

Developer Integration Snippets

1import requests 2import time 3import json 4 5api_key = "YOUR_API_KEY" 6url = "https://api.muapi.ai/workflow/run/ai-object-eraser" 7 8def poll_for_result(run_id): 9 url = f"https://api.muapi.ai/workflow/run/{run_id}/api-outputs" 10 headers = { 11 "Content-Type": "application/json", 12 "x-api-key": f"{api_key}", 13 } 14 while True: 15 response = requests.get(url, headers=headers) 16 result = response.json() 17 18 if result['status'] == 'completed': 19 return json.loads(result['outputs']) 20 21 if result['status'] == 'failed': 22 raise Exception(result.get('error', 'Generation failed')) 23 24 time.sleep(5) 25 26data = { 27 "schemas": { 28 "input_data": { 29 "x-order-properties": [ 30 "image_url", 31 "mask_image_url" 32 ], 33 "type": "object", 34 "properties": { 35 "image_url": { 36 "examples": [ 37 "https://d3adwkbyhxyrtq.cloudfront.net/ai-images/186/540230264117/be916c32-b646-45c3-99ac-7e19755cc97b.jpg" 38 ], 39 "description": "URL of the input image to erase from.", 40 "field": "image", 41 "type": "string", 42 "title": "Image URL", 43 "name": "image_url" 44 }, 45 "mask_image_url": { 46 "examples": [ 47 "https://d3adwkbyhxyrtq.cloudfront.net/aivideo/images/186/101261223520/mask-image-1752499424107.png" 48 ], 49 "description": "The URL of the binary mask image that represents the area that will be cleaned.", 50 "field": "image", 51 "type": "string", 52 "title": "Mask URL", 53 "name": "mask_image_url" 54 } 55 }, 56 "title": "BaseInput", 57 "required": [ 58 "image_url", 59 "mask_image_url" 60 ], 61 "endpoint_url": "ai-object-eraser" 62 }, 63 "output_data": { 64 "x-fal-order-properties": [ 65 "image" 66 ], 67 "type": "object", 68 "properties": { 69 "video": { 70 "examples": [ 71 "https://d3adwkbyhxyrtq.cloudfront.net/ai-images/186/432035650179/80aebb48-0073-45cd-9e2d-d5b75f9496f9.jpg" 72 ], 73 "description": "The generated image", 74 "title": "Image", 75 "comparison": false 76 } 77 }, 78 "title": "AIObjectEraser", 79 "required": [ 80 "image" 81 ] 82 } 83 } 84} 85 86headers = { 87 "Content-Type": "application/json", 88 "x-api-key": f"{api_key}", 89} 90 91response = requests.post(url, json=data, headers=headers) 92 93result = response.json() 94print("Queued:", result) 95 96outputs = poll_for_result(result["run_id"]) 97print("Completed:", outputs) 98

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