prunaai/flux.1-dev-lora:bbfeddb8 | Run with an API on Replicate

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prunaai /flux.1-dev-lora:bbfeddb8

Playground API

Input

prompt

*string

Shift + Return to add a new line

Prompt for generated image

aspect_ratio

string

Aspect ratio of the output image

Default: "1:1"

image

file

Input image for image to image mode. The aspect ratio of your output will match this image

prompt_strength

number

(minimum: 0, maximum: 1)

Prompt strength when using img2img. 1.0 corresponds to full destruction of information in image

Default: 0.8

num_outputs

integer

(minimum: 1, maximum: 4)

Number of outputs to generate

Default: 1

num_inference_steps

integer

Number of denoising steps. Recommended range is 28-50, and lower number of steps produce lower quality outputs, faster.

Default: 28

guidance

number

(minimum: 0, maximum: 10)

Guidance for generated image

Default: 3

seed

integer

Random seed. Set for reproducible generation

output_format

string

Format of the output images

Default: "jpg"

output_quality

integer

(minimum: 1, maximum: 100)

Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs

Default: 80

speed_mode

string

Speed optimization level

Default: "Juiced 🧃"

lora

string

Shift + Return to add a new line

Load LoRA weights. Supports HuggingFace URLs in the format <owner>/<model-name>[/<lora-weights-file.safetensors>]. For example, 'fofr/flux-pixar-cars'.

lora_scale

number

(minimum: -1, maximum: 3)

Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.

Default: 1

extra_lora

string

Shift + Return to add a new line

Load LoRA weights. Supports HuggingFace URLs in the format <owner>/<model-name>[/<lora-weights-file.safetensors>]. For example, 'fofr/flux-pixar-cars'.

extra_lora_scale

number

(minimum: -1, maximum: 3)

Determines how strongly the extra LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.

Default: 1

megapixels

string

Approximate number of megapixels for generated image

Default: "1"

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

npx create-replicate --model=prunaai/flux.1-dev-lora

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";
import fs from "node:fs";

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

Run prunaai/flux.1-dev-lora 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-dev-lora:bbfeddb88e5b906990d847b3f77c704a0d4069b3bbf8f7e11eab5145e4646df9",
  {
    input: {
      guidance: 3,
      lora_scale: 1,
      megapixels: "1",
      speed_mode: "Juiced 🧃",
      num_outputs: 1,
      aspect_ratio: "1:1",
      output_format: "jpg",
      output_quality: 80,
      prompt_strength: 0.8,
      extra_lora_scale: 1,
      num_inference_steps: 28
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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-dev-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/flux.1-dev-lora:bbfeddb88e5b906990d847b3f77c704a0d4069b3bbf8f7e11eab5145e4646df9",
    input={
        "guidance": 3,
        "lora_scale": 1,
        "megapixels": "1",
        "speed_mode": "Juiced 🧃",
        "num_outputs": 1,
        "aspect_ratio": "1:1",
        "output_format": "jpg",
        "output_quality": 80,
        "prompt_strength": 0.8,
        "extra_lora_scale": 1,
        "num_inference_steps": 28
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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-dev-lora 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": "prunaai/flux.1-dev-lora:bbfeddb88e5b906990d847b3f77c704a0d4069b3bbf8f7e11eab5145e4646df9",
    "input": {
      "guidance": 3,
      "lora_scale": 1,
      "megapixels": "1",
      "speed_mode": "Juiced 🧃",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "jpg",
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "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-dev-lora@sha256:bbfeddb88e5b906990d847b3f77c704a0d4069b3bbf8f7e11eab5145e4646df9 \
  -i 'guidance=3' \
  -i 'lora_scale=1' \
  -i 'megapixels="1"' \
  -i 'speed_mode="Juiced 🧃"' \
  -i 'num_outputs=1' \
  -i 'aspect_ratio="1:1"' \
  -i 'output_format="jpg"' \
  -i 'output_quality=80' \
  -i 'prompt_strength=0.8' \
  -i 'extra_lora_scale=1' \
  -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-dev-lora@sha256:bbfeddb88e5b906990d847b3f77c704a0d4069b3bbf8f7e11eab5145e4646df9
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "guidance": 3,
      "lora_scale": 1,
      "megapixels": "1",
      "speed_mode": "Juiced 🧃",
      "num_outputs": 1,
      "aspect_ratio": "1:1",
      "output_format": "jpg",
      "output_quality": 80,
      "prompt_strength": 0.8,
      "extra_lora_scale": 1,
      "num_inference_steps": 28
    }
  }' \
  http://localhost:5000/predictions

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

Output

No output yet! Press "Submit" to start a prediction.