Image Generation

Image generation using the playgroundai/playground-v2.5-1024px-aesthetic AI Model.

POST v1/replicate/imagegeneration

Image Generation

cURL

curl --request POST \
  --url http://localhost:9000/api/v1/replicate/imagegeneration \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: multipart/form-data' \
  --form 'prompt=<string>' \
  --form width=123 \
  --form height=123 \
  --form num_outputs=123 \
  --form 'negative_prompt=<string>' \
  --form num_inference_steps=123 \
  --form 'scheduler=<string>' \
  --form guidance_scale=123 \
  --form prompt_strength=123 \
  --form lora_scale=123 \
  --form image='@example-file' \
  --form mask='@example-file'

import requests

url = "http://localhost:9000/api/v1/replicate/imagegeneration"

files = {
    "image": ("example-file", open("example-file", "rb")),
    "mask": ("example-file", open("example-file", "rb"))
}
payload = {
    "prompt": "<string>",
    "width": "123",
    "height": "123",
    "num_outputs": "123",
    "negative_prompt": "<string>",
    "num_inference_steps": "123",
    "scheduler": "<string>",
    "guidance_scale": "123",
    "prompt_strength": "123",
    "lora_scale": "123"
}
headers = {"Authorization": "Bearer <token>"}

response = requests.post(url, data=payload, files=files, headers=headers)

print(response.text)

const form = new FormData();
form.append('prompt', '<string>');
form.append('width', '123');
form.append('height', '123');
form.append('num_outputs', '123');
form.append('negative_prompt', '<string>');
form.append('num_inference_steps', '123');
form.append('scheduler', '<string>');
form.append('guidance_scale', '123');
form.append('prompt_strength', '123');
form.append('lora_scale', '123');
form.append('image', '{
  "fileName": "example-file"
}');
form.append('mask', '{
  "fileName": "example-file"
}');

const options = {method: 'POST', headers: {Authorization: 'Bearer <token>'}};

options.body = form;

fetch('http://localhost:9000/api/v1/replicate/imagegeneration', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_PORT => "9000",
  CURLOPT_URL => "http://localhost:9000/api/v1/replicate/imagegeneration",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => "-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"prompt\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"width\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"height\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"num_outputs\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"negative_prompt\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"num_inference_steps\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"scheduler\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"guidance_scale\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"prompt_strength\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lora_scale\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n  \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"mask\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n  \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--",
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: multipart/form-data"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "http://localhost:9000/api/v1/replicate/imagegeneration"

	payload := strings.NewReader("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"prompt\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"width\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"height\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"num_outputs\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"negative_prompt\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"num_inference_steps\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"scheduler\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"guidance_scale\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"prompt_strength\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lora_scale\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n  \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"mask\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n  \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("http://localhost:9000/api/v1/replicate/imagegeneration")
  .header("Authorization", "Bearer <token>")
  .body("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"prompt\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"width\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"height\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"num_outputs\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"negative_prompt\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"num_inference_steps\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"scheduler\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"guidance_scale\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"prompt_strength\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lora_scale\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n  \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"mask\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n  \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--")
  .asString();

require 'uri'
require 'net/http'

url = URI("http://localhost:9000/api/v1/replicate/imagegeneration")

http = Net::HTTP.new(url.host, url.port)

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request.body = "-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"prompt\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"width\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"height\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"num_outputs\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"negative_prompt\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"num_inference_steps\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"scheduler\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"guidance_scale\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"prompt_strength\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lora_scale\"\r\n\r\n123\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n  \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"mask\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n  \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--"

response = http.request(request)
puts response.read_body

Query

model · string · default: "playgroundai/playground-v2.5-1024px-aesthetic" · required

The image model to be used;

Body

prompt · string · required

Input prompt

width · int · default: "1024"

Width of output image. Recommended 1024 or 1280

height · int · default: "1024"

Height of output image. Recommended 1024 or 1280

num_outputs · int · default: "1"

Number of images to output.

negative_prompt · string

Negative Input prompt

num_inference_steps · int · default: "25"

Number of denoising steps. 4 for best results

scheduler · string · default: "DPMSolver++"

Scheduler. DPMSolver++ or DPM++2MKarras is recommended for most cases

guidance_scale · float · default: "3.0"

Scale for classifier-free guidance

prompt_strength · float · default: "0.8"

Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image

lora_scale · float · default: "0.6"

LoRA additive scale. Only applicable on trained models.

image · file

Input image for img2img or inpaint mode

mask · file

Input image for img2img or inpaint mode