daanelson/stable-diffusion-speed-lab | Run with an API on Replicate

Input

prompt

string

Shift + Return to add a new line

👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic

Input prompt

Default: "a vision of paradise. unreal engine"

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 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: "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=daanelson/stable-diffusion-speed-lab

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";
import fs from "node:fs";

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

Run daanelson/stable-diffusion-speed-lab using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "daanelson/stable-diffusion-speed-lab:17c7552a84e231bece61d0ee1bf8c0f81553ea7d2d09d11ad40ed6c3949dbee6",
  {
    input: {
      prompt: "👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      guidance_scale: 7.5,
      num_inference_steps: 50
    }
  }
);

// 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

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

output = replicate.run(
    "daanelson/stable-diffusion-speed-lab:17c7552a84e231bece61d0ee1bf8c0f81553ea7d2d09d11ad40ed6c3949dbee6",
    input={
        "prompt": "👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 50
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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 daanelson/stable-diffusion-speed-lab 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": "daanelson/stable-diffusion-speed-lab:17c7552a84e231bece61d0ee1bf8c0f81553ea7d2d09d11ad40ed6c3949dbee6",
    "input": {
      "prompt": "👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic",
      "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.

Output

Generated in

1.9 seconds

Tweak itReport View full prediction

This output was created using a different version of the model, daanelson/stable-diffusion-speed-lab:05aca4ae.

Examples

View more examples

Run time and cost

This model costs approximately $0.0027 to run on Replicate, or 370 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 A100 (80GB) GPU hardware. Predictions typically complete within 2 seconds.