Prediction

cjwbw/stable-diffusion-v1-5:f370727477aa04d12d8c0b5c4e3a22399296c21cd18ff67cd7619710630fe3cb

Model

cjwbw/stable-diffusion-v1-5:f3707274

m5vocpwdmrf6nhocuibdb4yfdi

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

3.8s

Created

almost 3 years ago by @chenxwh

Input

width: 512
height: 512
prompt: multicolor hyperspace
scheduler: K-LMS
num_outputs: 1
guidance_scale: 7.5
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 512,
  "height": 512,
  "prompt": "multicolor hyperspace",
  "scheduler": "K-LMS",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

Install Replicate’s Node.js client library:

npm install replicate

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 cjwbw/stable-diffusion-v1-5 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cjwbw/stable-diffusion-v1-5:f370727477aa04d12d8c0b5c4e3a22399296c21cd18ff67cd7619710630fe3cb",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "multicolor hyperspace",
      scheduler: "K-LMS",
      num_outputs: 1,
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      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

Import the client:

import replicate

Run cjwbw/stable-diffusion-v1-5 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/stable-diffusion-v1-5:f370727477aa04d12d8c0b5c4e3a22399296c21cd18ff67cd7619710630fe3cb",
    input={
        "width": 512,
        "height": 512,
        "prompt": "multicolor hyperspace",
        "scheduler": "K-LMS",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "prompt_strength": 0.8,
        "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.

Run cjwbw/stable-diffusion-v1-5 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": "cjwbw/stable-diffusion-v1-5:f370727477aa04d12d8c0b5c4e3a22399296c21cd18ff67cd7619710630fe3cb",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "multicolor hyperspace",
      "scheduler": "K-LMS",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "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

3.9 seconds

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