fater-ai/flux-dev-controlnet-manual-input | Run with an API on Replicate

fater-ai / flux-dev-controlnet-manual-input

(Updated 4 months, 4 weeks ago)

Cold

Public
56 runs
A100 (80GB)

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

Default: ""

negative_prompt

string

Shift + Return to add a new line

Things you do not want to see in your image

Default: ""

guidance_scale

number

(minimum: 0, maximum: 5)

Guidance scale

Default: 3.5

steps

integer

(minimum: 1, maximum: 50)

Number of steps

Default: 28

control_type

string

Type of control net

Default: "depth"

control_strength

number

(minimum: 0, maximum: 3)

Strength of control net. Different controls work better with different strengths. Canny works best with 0.5, soft edge works best with 0.4, and depth works best between 0.5 and 0.75. If images are low quality, try reducing the strength and try reducing the guidance scale.

Default: 0.5

control_image

*file

Image to use with control net

image_to_image_strength

number

(minimum: 0, maximum: 1)

Strength of image to image control. 0 means none of the control image is used. 1 means the control image is returned used as is. Try values between 0 and 0.25 for best results.

Default: 0

depth_preprocessor

string

Preprocessor to use with depth control net

Default: "DepthAnything"

soft_edge_preprocessor

string

Preprocessor to use with soft edge control net

Default: "HED"

lora_url

string

Shift + Return to add a new line

Optional LoRA model to use. Give a URL to a HuggingFace .safetensors file, a Replicate .tar file or a CivitAI download link.

Default: ""

lora_strength

number

(minimum: -1, maximum: 3)

Strength of LoRA model

Default: 1

return_preprocessed_image

boolean

Return the preprocessed image used to control the generation process. Useful for debugging.

Default: false

output_format

string

Format of the output images

Default: "webp"

output_quality

integer

(minimum: 0, maximum: 100)

Quality of the output images, from 0 to 100. 100 is best quality, 0 is lowest quality.

Default: 80

seed

integer

Set a seed for reproducibility. Random by default.

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

npx create-replicate --model=fater-ai/flux-dev-controlnet-manual-input

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 fater-ai/flux-dev-controlnet-manual-input using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fater-ai/flux-dev-controlnet-manual-input:6e209aaa25f439afd29d8c09e207a79f98ba129e15dc5f6aad950d52385c0475",
  {
    input: {
      steps: 28,
      prompt: "",
      lora_url: "",
      control_type: "depth",
      lora_strength: 1,
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      negative_prompt: "",
      control_strength: 0.5,
      depth_preprocessor: "DepthAnything",
      soft_edge_preprocessor: "HED",
      image_to_image_strength: 0,
      return_preprocessed_image: false
    }
  }
);

// 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 fater-ai/flux-dev-controlnet-manual-input using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fater-ai/flux-dev-controlnet-manual-input:6e209aaa25f439afd29d8c09e207a79f98ba129e15dc5f6aad950d52385c0475",
    input={
        "steps": 28,
        "prompt": "",
        "lora_url": "",
        "control_type": "depth",
        "lora_strength": 1,
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "negative_prompt": "",
        "control_strength": 0.5,
        "depth_preprocessor": "DepthAnything",
        "soft_edge_preprocessor": "HED",
        "image_to_image_strength": 0,
        "return_preprocessed_image": False
    }
)
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 fater-ai/flux-dev-controlnet-manual-input 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": "fater-ai/flux-dev-controlnet-manual-input:6e209aaa25f439afd29d8c09e207a79f98ba129e15dc5f6aad950d52385c0475",
    "input": {
      "steps": 28,
      "prompt": "",
      "lora_url": "",
      "control_type": "depth",
      "lora_strength": 1,
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "negative_prompt": "",
      "control_strength": 0.5,
      "depth_preprocessor": "DepthAnything",
      "soft_edge_preprocessor": "HED",
      "image_to_image_strength": 0,
      "return_preprocessed_image": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

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

Run time and cost

This model runs on Nvidia A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.