anotherjesse/controlnet-tile-test | Run with an API on Replicate

Cold

Public

4.7K runs

A100 (80GB)

Playground API Examples README Versions

Input

control_image

file

Optional Image to use for guidance based on tile

controlnet_conditioning_scale

number

Scale of conditioning for controlnet

Default: 1

prompt

string

Shift + Return to add a new line

Input prompt

Default: "photo of cjw person"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

strength

number

(minimum: 0, maximum: 1)

Strength

Default: 1

resolution

integer

Resolution of output image

Default: 512

num_outputs

integer

(minimum: 1, maximum: 10)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

scheduler

string

Choose a scheduler.

Default: "KerrasDPM"

disable_safety_check

boolean

Disable safety check. Use at your own risk!

Default: false

seed

integer

Random seed. Leave blank to randomize the seed

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

npx create-replicate --model=anotherjesse/controlnet-tile-test

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 anotherjesse/controlnet-tile-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "anotherjesse/controlnet-tile-test:7a9961fdf246f78d9f90ace9b08fc00757c1b5b3cf78a492b145bc68cd6e1dfa",
  {
    input: {
      prompt: "photo of cjw person",
      strength: 1,
      scheduler: "KerrasDPM",
      resolution: 512,
      num_outputs: 1,
      guidance_scale: 7.5,
      num_inference_steps: 50,
      disable_safety_check: false,
      controlnet_conditioning_scale: 1
    }
  }
);

// 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 anotherjesse/controlnet-tile-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "anotherjesse/controlnet-tile-test:7a9961fdf246f78d9f90ace9b08fc00757c1b5b3cf78a492b145bc68cd6e1dfa",
    input={
        "prompt": "photo of cjw person",
        "strength": 1,
        "scheduler": "KerrasDPM",
        "resolution": 512,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 50,
        "disable_safety_check": False,
        "controlnet_conditioning_scale": 1
    }
)

# The anotherjesse/controlnet-tile-test model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/anotherjesse/controlnet-tile-test/api#output-schema
    print(item)

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 anotherjesse/controlnet-tile-test 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": "anotherjesse/controlnet-tile-test:7a9961fdf246f78d9f90ace9b08fc00757c1b5b3cf78a492b145bc68cd6e1dfa",
    "input": {
      "prompt": "photo of cjw person",
      "strength": 1,
      "scheduler": "KerrasDPM",
      "resolution": 512,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "num_inference_steps": 50,
      "disable_safety_check": false,
      "controlnet_conditioning_scale": 1
    }
  }' \
  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 costs approximately $0.011 to run on Replicate, or 90 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 9 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.