cjwbw/wuerstchen:b9560f3a – Run with an API on Replicate

Version

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cjwbw /wuerstchen:b9560f3a

Playground API

Input

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

npx create-replicate --model=cjwbw/wuerstchen

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

const output = await replicate.run(
  "cjwbw/wuerstchen:b9560f3a74a29b08f7d07e129407e4b0cf5848a20b88baa5086abaaee3253e7b",
  {
    input: {
      prompt: "Epic drawing of a dog",
      num_images: 1,
      guidance_scale: 6,
      negative_prompt: "low resolution, low detail, bad quality, blurry",
      num_inference_steps: 60
    }
  }
);

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

output = replicate.run(
    "cjwbw/wuerstchen:b9560f3a74a29b08f7d07e129407e4b0cf5848a20b88baa5086abaaee3253e7b",
    input={
        "prompt": "Epic drawing of a dog",
        "num_images": 1,
        "guidance_scale": 6,
        "negative_prompt": "low resolution, low detail, bad quality, blurry",
        "num_inference_steps": 60
    }
)
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 cjwbw/wuerstchen 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": "b9560f3a74a29b08f7d07e129407e4b0cf5848a20b88baa5086abaaee3253e7b",
    "input": {
      "prompt": "Epic drawing of a dog",
      "num_images": 1,
      "guidance_scale": 6,
      "negative_prompt": "low resolution, low detail, bad quality, blurry",
      "num_inference_steps": 60
    }
  }' \
  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/cjwbw/wuerstchen@sha256:b9560f3a74a29b08f7d07e129407e4b0cf5848a20b88baa5086abaaee3253e7b \
  -i 'prompt="Epic drawing of a dog"' \
  -i 'num_images=1' \
  -i 'guidance_scale=6' \
  -i 'negative_prompt="low resolution, low detail, bad quality, blurry"' \
  -i 'num_inference_steps=60'

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/cjwbw/wuerstchen@sha256:b9560f3a74a29b08f7d07e129407e4b0cf5848a20b88baa5086abaaee3253e7b
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "prompt": "Epic drawing of a dog",
      "num_images": 1,
      "guidance_scale": 6,
      "negative_prompt": "low resolution, low detail, bad quality, blurry",
      "num_inference_steps": 60
    }
  }' \
  http://localhost:5000/predictions

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

Output

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