Prediction

ID

gxbzy73bhd5kgjk7xngeyfiyl4

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

44.7s

Created

almost 2 years ago

Input

width: 1024
height: 1024
prompt: A TOK emoji skydiver in the sky with white skin and dark hair
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 3
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt: no helmet
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "A TOK emoji skydiver in the sky with white skin and dark hair",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 3,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "no helmet",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

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 pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "A TOK emoji skydiver in the sky with white skin and dark hair",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 3,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "no helmet",
      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.

pip install replicate

Import the client:

import replicate

Run pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "A TOK emoji skydiver in the sky with white skin and dark hair",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 3,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "no helmet",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run pielgrin/sdxl-omaji-sport 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": "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "A TOK emoji skydiver in the sky with white skin and dark hair",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 3,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "no helmet",
      "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

44.7 seconds

pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448

Prediction

Model

ID

lzoj3clbbybgwfoxixzmolv3cy

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

44.5s

Created

almost 2 years ago

Input

width: 1024
height: 1024
prompt: A TOK emoji skydiver in the sky with white skin and dark hair
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 3
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt: no helmet
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "A TOK emoji skydiver in the sky with white skin and dark hair",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 3,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "no helmet",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

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 pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "A TOK emoji skydiver in the sky with white skin and dark hair",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 3,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "no helmet",
      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.

pip install replicate

Import the client:

import replicate

Run pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "A TOK emoji skydiver in the sky with white skin and dark hair",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 3,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "no helmet",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run pielgrin/sdxl-omaji-sport 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": "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "A TOK emoji skydiver in the sky with white skin and dark hair",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 3,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "no helmet",
      "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

44.6 seconds

pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448

Prediction

Model

ID

mstf3ctbzi4i7wvbymm27yra5m

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

44.5s

Created

almost 2 years ago

Input

width: 1024
height: 1024
prompt: A TOK emoji surfer with brown skin and pink hair surfing a wave
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 3
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "A TOK emoji surfer with brown skin and pink hair surfing a wave",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 3,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

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 pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "A TOK emoji surfer with brown skin and pink hair surfing a wave",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 3,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      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.

pip install replicate

Import the client:

import replicate

Run pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "A TOK emoji surfer with brown skin and pink hair surfing a wave",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 3,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run pielgrin/sdxl-omaji-sport 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": "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "A TOK emoji surfer with brown skin and pink hair surfing a wave",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 3,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "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

44.5 seconds

pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448

Prediction

Model

ID

hhwyqutbye6duhleai7iitr6gu

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

44.8s

Created

almost 2 years ago

Input

width: 1024
height: 1024
prompt: a photo of TOK emoji dancing in a club
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 3
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "a photo of TOK emoji dancing in a club",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 3,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

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 pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "a photo of TOK emoji dancing in a club",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 3,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      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.

pip install replicate

Import the client:

import replicate

Run pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "a photo of TOK emoji dancing in a club",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 3,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run pielgrin/sdxl-omaji-sport 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": "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "a photo of TOK emoji dancing in a club",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 3,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "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

44.8 seconds

pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448

Prediction

Model

ID

yr4vtstb2jzwi6gudgacsos5pe

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

44.6s

Created

almost 2 years ago

Input

width: 1024
height: 1024
prompt: a photo of TOK emoji running
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 3
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "a photo of TOK emoji running",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 3,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

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 pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "a photo of TOK emoji running",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 3,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      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.

pip install replicate

Import the client:

import replicate

Run pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "a photo of TOK emoji running",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 3,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run pielgrin/sdxl-omaji-sport 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": "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "a photo of TOK emoji running",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 3,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "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

44.7 seconds

pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448

Prediction

Model

ID

ltqv2d3blptd2457g5bophbquu

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

59.3s

Created

almost 2 years ago

Input

width: 1024
height: 1024
prompt: An alien TOK emoji getting out of his spaceship
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 4
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt: no logo, no text
prompt_strength: 0.8
num_inference_steps: 50

{
  "width": 1024,
  "height": 1024,
  "prompt": "An alien TOK emoji getting out of his spaceship",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 4,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "no logo, no text",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

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 pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "An alien TOK emoji getting out of his spaceship",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 4,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "no logo, no text",
      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.

pip install replicate

Import the client:

import replicate

Run pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "An alien TOK emoji getting out of his spaceship",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 4,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "no logo, no text",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run pielgrin/sdxl-omaji-sport 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": "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "An alien TOK emoji getting out of his spaceship",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 4,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "no logo, no text",
      "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

59.4 seconds

pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448

Prediction

Model

ID

iew3hllbqbrntjxadivaypxas4

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

45.0s

Created

almost 2 years ago

Input

mask: null
image: null
width: 1024
height: 1024
prompt: a photo of a TOK emoji riding a BMX bike during Olympics 2024
refine: no_refiner
scheduler: K_EULER
lora_scale: 0.6
num_outputs: 3
guidance_scale: 7.5
apply_watermark
high_noise_frac: 0.8
negative_prompt: no yellow face
prompt_strength: 0.8
num_inference_steps: 50

{
  "mask": null,
  "image": null,
  "width": 1024,
  "height": 1024,
  "prompt": "a photo of a TOK emoji riding a BMX bike during Olympics 2024",
  "refine": "no_refiner",
  "scheduler": "K_EULER",
  "lora_scale": 0.6,
  "num_outputs": 3,
  "guidance_scale": 7.5,
  "apply_watermark": true,
  "high_noise_frac": 0.8,
  "negative_prompt": "no yellow face",
  "prompt_strength": 0.8,
  "num_inference_steps": 50
}

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 pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "a photo of a TOK emoji riding a BMX bike during Olympics 2024",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 3,
      guidance_scale: 7.5,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "no yellow face",
      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.

pip install replicate

Import the client:

import replicate

Run pielgrin/sdxl-omaji-sport using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "a photo of a TOK emoji riding a BMX bike during Olympics 2024",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 3,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "no yellow face",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)
print(output)

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

Run pielgrin/sdxl-omaji-sport 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": "pielgrin/sdxl-omaji-sport:f028d5da8a801a4d029b330f0330226af687457b0d267e27f0ad6025afa39448",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "a photo of a TOK emoji riding a BMX bike during Olympics 2024",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 3,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "no yellow face",
      "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

45.0 seconds