lucataco / shiba-diffusion

Shiba stable diffusion model

Cold

Public
690 runs
A100 (80GB)
GitHub
License

Iterate in the playground

Run with an API

Playground API Examples README Versions

Prediction

lucataco/shiba-diffusion:849b3b198a022bdaab1c528ac3eac5930852d1c545bf9c4dc8077a9ad7dff0b3

Model

lucataco/shiba-diffusion:849b3b19

6hjgwhzk35cljp3ycjbztjmcjq

Status

Succeeded

Source

Web

Hardware

–

Total duration

3.2s

Created

about 2 years ago

Input

width: 512
height: 512
prompt: a shiba wearing a tuxedo
scheduler: K-LMS
num_outputs: 1
guidance_scale: 4
prompt_strength: 1
num_inference_steps: 30

{
  "width": 512,
  "height": 512,
  "prompt": "a shiba wearing a tuxedo",
  "scheduler": "K-LMS",
  "num_outputs": 1,
  "guidance_scale": 4,
  "prompt_strength": 1,
  "num_inference_steps": 30
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run lucataco/shiba-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/shiba-diffusion:849b3b198a022bdaab1c528ac3eac5930852d1c545bf9c4dc8077a9ad7dff0b3",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "a shiba wearing a tuxedo",
      scheduler: "K-LMS",
      num_outputs: 1,
      guidance_scale: 4,
      prompt_strength: 1,
      num_inference_steps: 30
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run lucataco/shiba-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/shiba-diffusion:849b3b198a022bdaab1c528ac3eac5930852d1c545bf9c4dc8077a9ad7dff0b3",
    input={
        "width": 512,
        "height": 512,
        "prompt": "a shiba wearing a tuxedo",
        "scheduler": "K-LMS",
        "num_outputs": 1,
        "guidance_scale": 4,
        "prompt_strength": 1,
        "num_inference_steps": 30
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run lucataco/shiba-diffusion 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": "lucataco/shiba-diffusion:849b3b198a022bdaab1c528ac3eac5930852d1c545bf9c4dc8077a9ad7dff0b3",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "a shiba wearing a tuxedo",
      "scheduler": "K-LMS",
      "num_outputs": 1,
      "guidance_scale": 4,
      "prompt_strength": 1,
      "num_inference_steps": 30
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

Generated in

3.2 seconds

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