prompthunt/cog-with-tile – Run with an API on Replicate

prompthunt / cog-with-tile

Cold

Public
50 runs
L40S

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

weights

string

Shift + Return to add a new line

Weights url

control_image

file

Optional Image to use for guidance based on posenet

pose_image

file

Direct Pose image to use for guidance based on posenet, if available, ignores control_image

image

file

Optional Image to use for img2img guidance

mask

file

Optional Mask to use for legacy inpainting

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. Supported embeddings: realisticvision-negative-embedding, EasyNegative, FastNegativeV2, BadDream, ng_deepnegative_v1_75t, UnrealisticDream, negative_hand-neg

Default: ""

width

integer

Width of output image

Default: 512

height

integer

Height of output image

Default: 512

num_outputs

integer

(minimum: 1, maximum: 40)

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: 50)

Scale for classifier-free guidance

Default: 7.5

prompt_strength

number

(minimum: 0, maximum: 1)

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

Default: 0.8

scheduler

string

Choose a scheduler.

Default: "DPMSolverMultistep"

seed

integer

Random seed. Leave blank to randomize the seed

should_swap_face

boolean

Should swap face

Default: false

source_image

file

Source image for face swap

show_debug_images

boolean

Show debug images

Default: false

prompt_2

string

Shift + Return to add a new line

Input prompt

prompt_3

string

Shift + Return to add a new line

Input prompt

prompt_4

string

Shift + Return to add a new line

Input prompt

pose_image_2

file

Direct Pose image to use for guidance based on posenet, if available, ignores control_image

pose_image_3

file

Direct Pose image to use for guidance based on posenet, if available, ignores control_image

pose_image_4

file

Direct Pose image to use for guidance based on posenet, if available, ignores control_image

tile_strength

number

Tile strength

Default: 0.2

tile_steps

integer

Tile steps

Default: 32

tile_scale

number

Tile scale

Default: 1.5

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

npx create-replicate --model=prompthunt/cog-with-tile

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 prompthunt/cog-with-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prompthunt/cog-with-tile:8fc68c62a972ffb27de1823a28633dd27ae4323fb85f28b743d0ca82756f52e9",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "photo of cjw person",
      scheduler: "DPMSolverMultistep",
      tile_scale: 1.5,
      tile_steps: 32,
      num_outputs: 1,
      tile_strength: 0.2,
      guidance_scale: 7.5,
      negative_prompt: "",
      prompt_strength: 0.8,
      should_swap_face: false,
      show_debug_images: false,
      num_inference_steps: 50
    }
  }
);

// 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 prompthunt/cog-with-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prompthunt/cog-with-tile:8fc68c62a972ffb27de1823a28633dd27ae4323fb85f28b743d0ca82756f52e9",
    input={
        "width": 512,
        "height": 512,
        "prompt": "photo of cjw person",
        "scheduler": "DPMSolverMultistep",
        "tile_scale": 1.5,
        "tile_steps": 32,
        "num_outputs": 1,
        "tile_strength": 0.2,
        "guidance_scale": 7.5,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "should_swap_face": False,
        "show_debug_images": False,
        "num_inference_steps": 50
    }
)
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 prompthunt/cog-with-tile 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": "prompthunt/cog-with-tile:8fc68c62a972ffb27de1823a28633dd27ae4323fb85f28b743d0ca82756f52e9",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "photo of cjw person",
      "scheduler": "DPMSolverMultistep",
      "tile_scale": 1.5,
      "tile_steps": 32,
      "num_outputs": 1,
      "tile_strength": 0.2,
      "guidance_scale": 7.5,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "should_swap_face": false,
      "show_debug_images": false,
      "num_inference_steps": 50
    }
  }' \
  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 $0.026 to run on Replicate, or 38 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 L40S GPU hardware. Predictions typically complete within 27 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.