fenglinglwb/large-hole-image-inpainting:41462f3a

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fenglinglwb /large-hole-image-inpainting:41462f3a

Playground API

Input

image

*file

Input image to inpaint. Should be 512x512 for optimal results.

mask

file

Optional mask (same size as image) that overlays image. Should be black in areas that you wish to inpaint. If left blank, a random mask will be generated over the image.

model

string

Select which model to use: Places or CelebA-HQ-512

Default: "places"

truncation_psi

number

Truncation psi. Improve image quality at the cost of output diversity/variation; truncation psi ψ = 1 means no truncation, ψ = 0 means no diversity.

Default: 1

noise_mode

string

Noise mode

Default: "const"

seed

integer

(minimum: -1, maximum: 4294967295)

Seed for random number generator to encourage diverse results. If -1, a random seed will be chosen. (minimum: -1; maximum: 4294967295)

Default: -1

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

npx create-replicate --model=fenglinglwb/large-hole-image-inpainting

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 fenglinglwb/large-hole-image-inpainting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fenglinglwb/large-hole-image-inpainting:41462f3af4281f853a439a5af4a549acfaca7cacd9922dd4df6500352dc84d47",
  {
    input: {
      seed: -1,
      model: "places",
      noise_mode: "const",
      truncation_psi: 1
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", 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 fenglinglwb/large-hole-image-inpainting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fenglinglwb/large-hole-image-inpainting:41462f3af4281f853a439a5af4a549acfaca7cacd9922dd4df6500352dc84d47",
    input={
        "seed": -1,
        "model": "places",
        "noise_mode": "const",
        "truncation_psi": 1
    }
)
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 fenglinglwb/large-hole-image-inpainting 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": "41462f3af4281f853a439a5af4a549acfaca7cacd9922dd4df6500352dc84d47",
    "input": {
      "seed": -1,
      "model": "places",
      "noise_mode": "const",
      "truncation_psi": 1
    }
  }' \
  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/fenglinglwb/large-hole-image-inpainting@sha256:41462f3af4281f853a439a5af4a549acfaca7cacd9922dd4df6500352dc84d47 \
  -i 'seed=-1' \
  -i 'model="places"' \
  -i 'noise_mode="const"' \
  -i 'truncation_psi=1'

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/fenglinglwb/large-hole-image-inpainting@sha256:41462f3af4281f853a439a5af4a549acfaca7cacd9922dd4df6500352dc84d47
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": -1,
      "model": "places",
      "noise_mode": "const",
      "truncation_psi": 1
    }
  }' \
  http://localhost:5000/predictions

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

Output

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