peter942/squishy_olivier – Run with an API on Replicate

peter942 / squishy_olivier

Cold

Public
401 runs
T4

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: "squishy wishy"

negative_prompt

string

Shift + Return to add a new line

Specify things to not see in the output

image

file

A starting image from which to generate variations (aka 'img2img'). If this input is set, the `width` and `height` inputs are ignored and the output will have the same dimensions as the input image.

width

integer

Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

height

integer

Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

prompt_strength

number

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

Default: 0.8

num_outputs

integer

(minimum: 1, maximum: 4)

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: "DDIM"

seed

integer

Random seed. Leave blank to randomize the seed

disable_safety_check

boolean

Disable safety check. Use at your own risk!

Default: false

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

npx create-replicate --model=peter942/squishy_olivier

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 peter942/squishy_olivier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "peter942/squishy_olivier:c00d0ffc9a42a154ea6c6a78afb76429536e169fd9ba6fe14eb93130cd5473a3",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "squishy wishy",
      scheduler: "DDIM",
      num_outputs: 1,
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50,
      disable_safety_check: false
    }
  }
);
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 peter942/squishy_olivier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "peter942/squishy_olivier:c00d0ffc9a42a154ea6c6a78afb76429536e169fd9ba6fe14eb93130cd5473a3",
    input={
        "width": 512,
        "height": 512,
        "prompt": "squishy wishy",
        "scheduler": "DDIM",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "prompt_strength": 0.8,
        "num_inference_steps": 50,
        "disable_safety_check": 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 peter942/squishy_olivier 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": "c00d0ffc9a42a154ea6c6a78afb76429536e169fd9ba6fe14eb93130cd5473a3",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "squishy wishy",
      "scheduler": "DDIM",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50,
      "disable_safety_check": false
    }
  }' \
  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/peter942/squishy_olivier@sha256:c00d0ffc9a42a154ea6c6a78afb76429536e169fd9ba6fe14eb93130cd5473a3 \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="squishy wishy"' \
  -i 'scheduler="DDIM"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=50' \
  -i 'disable_safety_check=false'

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/peter942/squishy_olivier@sha256:c00d0ffc9a42a154ea6c6a78afb76429536e169fd9ba6fe14eb93130cd5473a3
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "squishy wishy",
      "scheduler": "DDIM",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50,
      "disable_safety_check": false
    }
  }' \
  http://localhost:5000/predictions

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

Output

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

Run time and cost

This model costs approximately $0.0013 to run on Replicate, or 769 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 T4 GPU hardware. Predictions typically complete within 6 seconds.

Readme

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Model description

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Intended use

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Ethical considerations

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Caveats and recommendations

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