Prediction

asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033

Model

asiryan/flux-schnell:21b2d145

ID

kmyqb67529rj20cj18s9f51mx4

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

80.2s

Created

11 months ago

Input

width: 1024
height: 1024
prompt: a tiny astronaut hatching from an egg on the moon
num_outputs: 1
output_format: png
output_quality: 100
prompt_strength: 0.8
num_inference_steps: 4

{
  "width": 1024,
  "height": 1024,
  "prompt": "a tiny astronaut hatching from an egg on the moon",
  "num_outputs": 1,
  "output_format": "png",
  "output_quality": 100,
  "prompt_strength": 0.8,
  "num_inference_steps": 4
}

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 asiryan/flux-schnell using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "a tiny astronaut hatching from an egg on the moon",
      num_outputs: 1,
      output_format: "png",
      output_quality: 100,
      prompt_strength: 0.8,
      num_inference_steps: 4
    }
  }
);

// 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 asiryan/flux-schnell using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "a tiny astronaut hatching from an egg on the moon",
        "num_outputs": 1,
        "output_format": "png",
        "output_quality": 100,
        "prompt_strength": 0.8,
        "num_inference_steps": 4
    }
)

# 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 asiryan/flux-schnell 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": "asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "a tiny astronaut hatching from an egg on the moon",
      "num_outputs": 1,
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/asiryan/flux-schnell@sha256:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033 \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="a tiny astronaut hatching from an egg on the moon"' \
  -i 'num_outputs=1' \
  -i 'output_format="png"' \
  -i 'output_quality=100' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=4'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/asiryan/flux-schnell@sha256:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "a tiny astronaut hatching from an egg on the moon",
      "num_outputs": 1,
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

Generated in

3.4 seconds

Tweak it Iterate in playground ShareReport

Prediction

asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033

Model

asiryan/flux-schnell:21b2d145

ID

pthsgkvs9xrj00cj18yb5ywen4

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

2.8s

Created

11 months ago

Input

width: 1024
height: 1024
prompt: beautiful blonde girl on the beach
num_outputs: 1
output_format: png
output_quality: 100
prompt_strength: 0.8
num_inference_steps: 4

{
  "width": 1024,
  "height": 1024,
  "prompt": "beautiful blonde girl on the beach",
  "num_outputs": 1,
  "output_format": "png",
  "output_quality": 100,
  "prompt_strength": 0.8,
  "num_inference_steps": 4
}

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 asiryan/flux-schnell using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "beautiful blonde girl on the beach",
      num_outputs: 1,
      output_format: "png",
      output_quality: 100,
      prompt_strength: 0.8,
      num_inference_steps: 4
    }
  }
);

// 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 asiryan/flux-schnell using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "beautiful blonde girl on the beach",
        "num_outputs": 1,
        "output_format": "png",
        "output_quality": 100,
        "prompt_strength": 0.8,
        "num_inference_steps": 4
    }
)

# 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 asiryan/flux-schnell 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": "asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "beautiful blonde girl on the beach",
      "num_outputs": 1,
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/asiryan/flux-schnell@sha256:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033 \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="beautiful blonde girl on the beach"' \
  -i 'num_outputs=1' \
  -i 'output_format="png"' \
  -i 'output_quality=100' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=4'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/asiryan/flux-schnell@sha256:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "beautiful blonde girl on the beach",
      "num_outputs": 1,
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

Generated in

2.8 seconds

Tweak it Iterate in playground ShareReport

Prediction

asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033

Model

asiryan/flux-schnell:21b2d145

ID

s1xepw80q9rj20cj18zaefg420

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

2.7s

Created

11 months ago

Input

width: 1024
height: 1024
prompt: black forest gateau cake spelling out the words "FLUX SCHNELL", tasty, food photography, dynamic shot
num_outputs: 1
output_format: png
output_quality: 100
prompt_strength: 0.8
num_inference_steps: 4

{
  "width": 1024,
  "height": 1024,
  "prompt": "black forest gateau cake spelling out the words \"FLUX SCHNELL\", tasty, food photography, dynamic shot",
  "num_outputs": 1,
  "output_format": "png",
  "output_quality": 100,
  "prompt_strength": 0.8,
  "num_inference_steps": 4
}

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 asiryan/flux-schnell using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "black forest gateau cake spelling out the words \"FLUX SCHNELL\", tasty, food photography, dynamic shot",
      num_outputs: 1,
      output_format: "png",
      output_quality: 100,
      prompt_strength: 0.8,
      num_inference_steps: 4
    }
  }
);

// 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 asiryan/flux-schnell using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "black forest gateau cake spelling out the words \"FLUX SCHNELL\", tasty, food photography, dynamic shot",
        "num_outputs": 1,
        "output_format": "png",
        "output_quality": 100,
        "prompt_strength": 0.8,
        "num_inference_steps": 4
    }
)

# 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 asiryan/flux-schnell 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": "asiryan/flux-schnell:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "black forest gateau cake spelling out the words \\"FLUX SCHNELL\\", tasty, food photography, dynamic shot",
      "num_outputs": 1,
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/asiryan/flux-schnell@sha256:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033 \
  -i 'width=1024' \
  -i 'height=1024' \
  -i $'prompt="black forest gateau cake spelling out the words \\"FLUX SCHNELL\\", tasty, food photography, dynamic shot"' \
  -i 'num_outputs=1' \
  -i 'output_format="png"' \
  -i 'output_quality=100' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=4'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/asiryan/flux-schnell@sha256:21b2d1458c4afe4ba7ad2ab56cc45b862736955bacaf993acfa173e3be1b8033
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "black forest gateau cake spelling out the words \\"FLUX SCHNELL\\", tasty, food photography, dynamic shot",
      "num_outputs": 1,
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "num_inference_steps": 4
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

Generated in

2.7 seconds

Tweak it Iterate in playground ShareReport

Prediction

Input

Output

Prediction

Input

Output

Prediction

Input

Output

Logs (kmyqb67529rj20cj18s9f51mx4)

Logs (pthsgkvs9xrj00cj18yb5ywen4)

Logs (s1xepw80q9rj20cj18zaefg420)