Back to Templates

Generate High-Quality Images with Replicate's Fire/Flux AI Model

Created by

Created by: Yaron Been || yaron-nofluff

Yaron Been

Last update

Last update 2 days ago

Share


Fire Flux Image Generator

Description

The image generation model tailored for local development and personal use

Overview

This n8n workflow integrates with the Replicate API to use the fire/flux model. This powerful AI model can generate high-quality image content based on your inputs.

Features

  • Easy integration with Replicate API
  • Automated status checking and result retrieval
  • Support for all model parameters
  • Error handling and retry logic
  • Clean output formatting

Parameters

Required Parameters

  • prompt (string): Prompt for generated image

Optional Parameters

  • seed (integer, default: 0): Random seed. Set for reproducible generation
  • go_fast (boolean, default: True): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16
  • megapixels (string, default: 1): Approximate number of megapixels for generated image
  • num_outputs (integer, default: 1): Number of outputs to generate
  • aspect_ratio (string, default: 2:1): Aspect ratio for the generated image
  • output_format (string, default: png): Format of the output images
  • output_quality (integer, default: 80): Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs
  • num_inference_steps (integer, default: 4): Number of denoising steps. 4 is recommended, and lower number of steps produce lower quality outputs, faster.
  • disable_safety_checker (boolean, default: False): Disable safety checker for generated images.

How to Use

  1. Set up your Replicate API key in the workflow
  2. Configure the required parameters for your use case
  3. Run the workflow to generate image content
  4. Access the generated output from the final node

API Reference

Requirements

  • Replicate API key
  • n8n instance
  • Basic understanding of image generation parameters