prunaai/stable-diffusion-cheetah:bce45013 – Run with an API on Replicate

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prunaai /stable-diffusion-cheetah:bce45013

Playground API

Input

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

npx create-replicate --model=prunaai/stable-diffusion-cheetah

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 prunaai/stable-diffusion-cheetah using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prunaai/stable-diffusion-cheetah:bce45013ec9e0034b87dbb81bca9458b57922c0dbdae6901603780ba6ca15fb6",
  {
    input: {
      seed: -1,
      prompt: "a red apple on a table",
      go_fast_af: false,
      num_images: 1,
      image_width: 512,
      image_height: 512,
      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 prunaai/stable-diffusion-cheetah using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/stable-diffusion-cheetah:bce45013ec9e0034b87dbb81bca9458b57922c0dbdae6901603780ba6ca15fb6",
    input={
        "seed": -1,
        "prompt": "a red apple on a table",
        "go_fast_af": False,
        "num_images": 1,
        "image_width": 512,
        "image_height": 512,
        "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 prunaai/stable-diffusion-cheetah 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": "bce45013ec9e0034b87dbb81bca9458b57922c0dbdae6901603780ba6ca15fb6",
    "input": {
      "seed": -1,
      "prompt": "a red apple on a table",
      "go_fast_af": false,
      "num_images": 1,
      "image_width": 512,
      "image_height": 512,
      "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/prunaai/stable-diffusion-cheetah@sha256:bce45013ec9e0034b87dbb81bca9458b57922c0dbdae6901603780ba6ca15fb6 \
  -i 'seed=-1' \
  -i 'prompt="a red apple on a table"' \
  -i 'go_fast_af=false' \
  -i 'num_images=1' \
  -i 'image_width=512' \
  -i 'image_height=512' \
  -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/prunaai/stable-diffusion-cheetah@sha256:bce45013ec9e0034b87dbb81bca9458b57922c0dbdae6901603780ba6ca15fb6
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": -1,
      "prompt": "a red apple on a table",
      "go_fast_af": false,
      "num_images": 1,
      "image_width": 512,
      "image_height": 512,
      "guidance_scale": 7.5,
      "num_inference_steps": 50
    }
  }' \
  http://localhost:5000/predictions

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

Output

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