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Training

Flux Dev Lora API Pricing Comparison

Enables text-to-image generation using custom LoRA models. Generate consistent characters, styles, or branded visuals with high quality and fast results.

Savings Alert25% ↓Cheaper than Fal.ai

Model Overview

Flux Dev LoRA is a cutting-edge text-to-image generation model designed to harness the power of custom LoRA models for stunning visual outputs. By integrating advanced AI algorithms with niche-specific LoRA weighting, this model enables users to generate consistent characters, styles, or even branded visuals with remarkable precision and artistic flair. Whether you are a digital artist, a content creator, or a developer looking for rapid prototyping, Flux Dev LoRA brings speed and reliability to the forefront of creative image generation. Built on a robust technical foundation, Flux Dev LoRA allows flexible input parameters including prompt descriptions, image dimensions, and the dynamic blending of multiple LoRA models. This integration means you can fine-tune the aesthetics of your output by adjusting weights and dimensions, ensuring each generation meets your exact vision. It stands out in the training category by delivering high-quality, maintained consistency across multiple images while keeping the cost per generation exceptionally low at just $0.015.

Interactive Savings Calculator

Estimate monthly API spend and compare absolute developer savings.

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

$150.00

$0.015
Fal.ai Cost

$200.00

$0.02
Estimated Monthly Savings$50.00
Annual Savings$600.00

Detailed Pricing Breakdown

ProviderEstimated RateNotes
muapiapp$0.015muapiapp offers this service at $0.015 per generation, making it 20-50% more affordable while delivering comparable or superior quality compared to competitors.
Fal.ai$0.02Fal.ai charges $0.02 per generation, meaning muapiapp is 20-50% cheaper than Fal.ai while maintaining high quality.
Replicate$0.02Replicate also charges $0.02 per generation, ensuring that muapiapp remains 20-50% more cost-effective with similar or better performance.

Developer Integration Snippets

1import requests 2import time 3import json 4 5api_key = "YOUR_API_KEY" 6url = "https://api.muapi.ai/workflow/run/flux-dev-lora" 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 "prompt", 31 "width", 32 "height", 33 "num_images", 34 "model_id" 35 ], 36 "type": "object", 37 "properties": { 38 "prompt": { 39 "examples": [ 40 "A female warrior in ornate armor standing on a cliff during sunset, flowing cape, wind blowing through her hair, detailed fantasy art style." 41 ], 42 "description": "Text prompt describing the image. The length of the prompt must be between 2 and 3000 characters.", 43 "type": "string", 44 "title": "Prompt", 45 "name": "prompt" 46 }, 47 "model_id": { 48 "examples": [ 49 { 50 "model": "civitai:119351@317153", 51 "weight": 1 52 } 53 ], 54 "title": "LoRA Ids", 55 "name": "model_id", 56 "type": "array", 57 "items": { 58 "type": "object", 59 "properties": { 60 "model": { 61 "type": "string", 62 "format": "url", 63 "title": "Model ID", 64 "description": "The Civitai LoRA model ID." 65 }, 66 "weight": { 67 "type": "number", 68 "title": "Weight", 69 "description": "A list of LoRA models to use for generation. Each item must include an `id` (e.g., \"civitai:1642876@1864626\") and a `weight` between 0 and 4. You can include up to 4 models. The `id` can be found in the Civitai model URL. These models will be applied with the specified weights by the Flux Dev system during image generation.", 70 "minValue": 0, 71 "maxValue": 4, 72 "step": 0.01, 73 "default": 1 74 } 75 } 76 }, 77 "description": "The unique identifier of a LoRA model hosted on Civitai, used by the Flux Dev image generation system. This ID tells Flux Dev which specific LoRA model to apply during generation. You can find the model ID in the Civitai model URL (e.g., model_id: civitai:1642876@1864626).", 78 "maxItems": 4 79 }, 80 "width": { 81 "title": "Width", 82 "name": "width", 83 "type": "int", 84 "description": "Width of the output image. The value must be divisible by 64, eg: 128...512, 576, 640...2048.", 85 "default": 1024, 86 "minValue": 128, 87 "maxValue": 2048, 88 "step": 64, 89 "isEdit": true 90 }, 91 "height": { 92 "title": "Height", 93 "name": "height", 94 "type": "int", 95 "description": "Height of the output image. The value must be divisible by 64, eg: 128...512, 576, 640...2048.", 96 "default": 1024, 97 "minValue": 128, 98 "maxValue": 2048, 99 "step": 64, 100 "isEdit": true 101 }, 102 "num_images": { 103 "title": "Number of images", 104 "name": "num_images", 105 "type": "int", 106 "description": "Number of images generated in single request. Each number will charge separately", 107 "default": 1, 108 "minValue": 1, 109 "maxValue": 4, 110 "step": 1, 111 "isEdit": true 112 } 113 }, 114 "title": "BaseInput", 115 "required": [ 116 "prompt", 117 "model_id" 118 ], 119 "endpoint_url": "flux_dev_lora_image" 120 }, 121 "output_data": { 122 "x-fal-order-properties": [ 123 "image" 124 ], 125 "type": "object", 126 "properties": { 127 "video": { 128 "examples": [ 129 "https://d3adwkbyhxyrtq.cloudfront.net/muapi/homepage/flux-dev-lora.avif" 130 ], 131 "description": "The generated image", 132 "title": "Image", 133 "comparison": false 134 } 135 }, 136 "title": "FluxDevLoRA", 137 "required": [ 138 "image" 139 ] 140 } 141 } 142} 143 144headers = { 145 "Content-Type": "application/json", 146 "x-api-key": f"{api_key}", 147} 148 149response = requests.post(url, json=data, headers=headers) 150 151result = response.json() 152print("Queued:", result) 153 154outputs = poll_for_result(result["run_id"]) 155print("Completed:", outputs) 156

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