Prediction

daanelson/stable-diffusion-speed-lab:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146

Model

daanelson/stable-diffusion-speed-lab:49055001

ID

rcia4uwu6bf7jbokhst44btuby

Status

Succeeded

Source

Web

Hardware

–

Total duration

1.9s

Created

over 2 years ago

Input

prompt: an illustration of a redwood grove, oil painting, detailed, ferns
num_outputs: 1
guidance_scale: 7.5
num_inference_steps: 50

{
  "prompt": "an illustration of a redwood grove, oil painting, detailed, ferns",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "num_inference_steps": 50
}

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 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:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146",
  {
    input: {
      prompt: "an illustration of a redwood grove, oil painting, detailed, ferns",
      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

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:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146",
    input={
        "prompt": "an illustration of a redwood grove, oil painting, detailed, ferns",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 50
    }
)
print(output)

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

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:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146",
    "input": {
      "prompt": "an illustration of a redwood grove, oil painting, detailed, ferns",
      "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 it ShareReport

Prediction

daanelson/stable-diffusion-speed-lab:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146

Model

daanelson/stable-diffusion-speed-lab:49055001

ID

rjne6sk3szbs7o2vthxnu356pa

Status

Succeeded

Source

Web

Hardware

–

Total duration

2.0s

Created

over 2 years ago by @anotherjesse

Input

seed: 100
prompt: 👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic
num_outputs: 1
guidance_scale: 7.5
num_inference_steps: 50

{
  "seed": 100,
  "prompt": "👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "num_inference_steps": 50
}

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 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:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146",
  {
    input: {
      seed: 100,
      prompt: "👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic",
      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

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:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146",
    input={
        "seed": 100,
        "prompt": "👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 50
    }
)
print(output)

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

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:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146",
    "input": {
      "seed": 100,
      "prompt": "👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic",
      "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 it ShareReport

Prediction

daanelson/stable-diffusion-speed-lab:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146

Model

daanelson/stable-diffusion-speed-lab:49055001

ID

wtl2het2lrfnjiemra7wzsp2dq

Status

Succeeded

Source

API

Hardware

–

Total duration

1.9s

Created

over 2 years ago

Input

prompt: landscape photography of fundatura ponorului by marc adamus, morning, mist, rays of light, beautiful
negative_prompt: bad anatomy, low res, blurry, bad features, noisy

{
  "prompt": "landscape photography of fundatura ponorului by marc adamus, morning, mist, rays of light, beautiful",
  "negative_prompt": "bad anatomy, low res, blurry, bad features, noisy"
}

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 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:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146",
  {
    input: {
      prompt: "landscape photography of fundatura ponorului by marc adamus, morning, mist, rays of light, beautiful",
      negative_prompt: "bad anatomy, low res, blurry, bad features, noisy"
    }
  }
);

// 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 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:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146",
    input={
        "prompt": "landscape photography of fundatura ponorului by marc adamus, morning, mist, rays of light, beautiful",
        "negative_prompt": "bad anatomy, low res, blurry, bad features, noisy"
    }
)
print(output)

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

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:4905500191572a49169c7b2eef219b3ac7a3da0beeaeb0f132e8df8766de6146",
    "input": {
      "prompt": "landscape photography of fundatura ponorului by marc adamus, morning, mist, rays of light, beautiful",
      "negative_prompt": "bad anatomy, low res, blurry, bad features, noisy"
    }
  }' \
  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 it ShareReport

Prediction

daanelson/stable-diffusion-speed-lab:05aca4aea54103353540845627a4ac45412195df3b099f9f4d932f58a5ccb820

Model

daanelson/stable-diffusion-speed-lab:05aca4ae

ID

li3j67gftna5xb7a3lin2tn7iq

Status

Succeeded

Source

Web

Hardware

–

Total duration

1.9s

Created

over 2 years ago

Input

seed: 100
prompt: A vision of paradise, Unreal Engine
scheduler: DPMSolverMultistep
num_outputs: 1
guidance_scale: 7.5
num_inference_steps: 50

{
  "seed": 100,
  "prompt": "A vision of paradise, Unreal Engine",
  "scheduler": "DPMSolverMultistep",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "num_inference_steps": 50
}

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 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:05aca4aea54103353540845627a4ac45412195df3b099f9f4d932f58a5ccb820",
  {
    input: {
      seed: 100,
      prompt: "A vision of paradise, Unreal Engine",
      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

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:05aca4aea54103353540845627a4ac45412195df3b099f9f4d932f58a5ccb820",
    input={
        "seed": 100,
        "prompt": "A vision of paradise, Unreal Engine",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 50
    }
)
print(output)

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

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:05aca4aea54103353540845627a4ac45412195df3b099f9f4d932f58a5ccb820",
    "input": {
      "seed": 100,
      "prompt": "A vision of paradise, Unreal Engine",
      "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

1.8 seconds

Tweak it ShareReport

Prediction

daanelson/stable-diffusion-speed-lab:05aca4aea54103353540845627a4ac45412195df3b099f9f4d932f58a5ccb820

Model

daanelson/stable-diffusion-speed-lab:05aca4ae

ID

dk6eqsktbnajzcbdwwy4vydqra

Status

Succeeded

Source

Web

Hardware

–

Total duration

1.9s

Created

over 2 years ago by @daanelson

Input

prompt: 👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic
scheduler: DPMSolverMultistep
num_outputs: 1
guidance_scale: 7.5
num_inference_steps: 50

{
  "prompt": "👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic",
  "scheduler": "DPMSolverMultistep",
  "num_outputs": 1,
  "guidance_scale": 7.5,
  "num_inference_steps": 50
}

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 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:05aca4aea54103353540845627a4ac45412195df3b099f9f4d932f58a5ccb820",
  {
    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

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:05aca4aea54103353540845627a4ac45412195df3b099f9f4d932f58a5ccb820",
    input={
        "prompt": "👨‍🚀🦅, digital Art, Greg rutkowski, Trending artstation,cinematographic",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 50
    }
)
print(output)

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

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:05aca4aea54103353540845627a4ac45412195df3b099f9f4d932f58a5ccb820",
    "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.