stability-ai/stable-diffusion-inpainting:c2172c44

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stability-ai /stable-diffusion-inpainting:c2172c44

Playground API

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: "a vision of paradise. unreal engine"

image

*file

Initial image to generate variations of. Will be resized to height x width

mask

*file

Black and white image to use as mask for inpainting over the image provided. White pixels are inpainted and black pixels are preserved.

height

integer

Height of generated image in pixels. Needs to be a multiple of 64

Default: 512

width

integer

Width of generated image in pixels. Needs to be a multiple of 64

Default: 512

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to generate.

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: "DPMSolverMultistep"

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=stability-ai/stable-diffusion-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 stability-ai/stable-diffusion-inpainting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "stability-ai/stable-diffusion-inpainting:c2172c447eb69551b59f62fd2d61dd84054e9fb7bc8a42fbe398c2a7a072ed68",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "a vision of paradise. unreal engine",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      guidance_scale: 7.5,
      num_inference_steps: 50
    }
  }
);
console.log(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 stability-ai/stable-diffusion-inpainting using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "stability-ai/stable-diffusion-inpainting:c2172c447eb69551b59f62fd2d61dd84054e9fb7bc8a42fbe398c2a7a072ed68",
    input={
        "width": 512,
        "height": 512,
        "prompt": "a vision of paradise. unreal engine",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "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 stability-ai/stable-diffusion-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": "c2172c447eb69551b59f62fd2d61dd84054e9fb7bc8a42fbe398c2a7a072ed68",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "a vision of paradise. unreal engine",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "num_inference_steps": 50
    }
  }' \
  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/stability-ai/stable-diffusion-inpainting@sha256:c2172c447eb69551b59f62fd2d61dd84054e9fb7bc8a42fbe398c2a7a072ed68 \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="a vision of paradise. unreal engine"' \
  -i 'scheduler="DPMSolverMultistep"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'num_inference_steps=50'

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/stability-ai/stable-diffusion-inpainting@sha256:c2172c447eb69551b59f62fd2d61dd84054e9fb7bc8a42fbe398c2a7a072ed68
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "a vision of paradise. unreal engine",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "num_inference_steps": 50
    }
  }' \
  http://localhost:5000/predictions

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

Output

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