prunaai/flux.1-juiced:aecf596c – Run with an API on Replicate

Version

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prunaai /flux.1-juiced:aecf596c

Playground API

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=prunaai/flux.1-juiced

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run prunaai/flux.1-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prunaai/flux.1-juiced:aecf596c357894b2b9f63cb5cc069b79f346c58281f76ec0e508aff52e3d337a",
  {
    input: {
      seed: 42,
      speed: 0.5,
      guidance: 7.5,
      image_size: 1024,
      aspect_ratio: "1:1",
      output_format: "png",
      output_quality: 80,
      prompt_strength: 0.8,
      num_inference_steps: 28
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run prunaai/flux.1-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/flux.1-juiced:aecf596c357894b2b9f63cb5cc069b79f346c58281f76ec0e508aff52e3d337a",
    input={
        "seed": 42,
        "speed": 0.5,
        "guidance": 7.5,
        "image_size": 1024,
        "aspect_ratio": "1:1",
        "output_format": "png",
        "output_quality": 80,
        "prompt_strength": 0.8,
        "num_inference_steps": 28
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run prunaai/flux.1-juiced using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "aecf596c357894b2b9f63cb5cc069b79f346c58281f76ec0e508aff52e3d337a",
    "input": {
      "seed": 42,
      "speed": 0.5,
      "guidance": 7.5,
      "image_size": 1024,
      "aspect_ratio": "1:1",
      "output_format": "png",
      "output_quality": 80,
      "prompt_strength": 0.8,
      "num_inference_steps": 28
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/prunaai/flux.1-juiced@sha256:aecf596c357894b2b9f63cb5cc069b79f346c58281f76ec0e508aff52e3d337a \
  -i 'seed=42' \
  -i 'speed=0.5' \
  -i 'guidance=7.5' \
  -i 'image_size=1024' \
  -i 'aspect_ratio="1:1"' \
  -i 'output_format="png"' \
  -i 'output_quality=80' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=28'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/prunaai/flux.1-juiced@sha256:aecf596c357894b2b9f63cb5cc069b79f346c58281f76ec0e508aff52e3d337a
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 42,
      "speed": 0.5,
      "guidance": 7.5,
      "image_size": 1024,
      "aspect_ratio": "1:1",
      "output_format": "png",
      "output_quality": 80,
      "prompt_strength": 0.8,
      "num_inference_steps": 28
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

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