witzelfitz / gardenflux

Warm

Public
154 runs
H100

Run with an API

Playground API Examples README Versions

Prediction

witzelfitz/gardenflux:b50ad39c082c82d614b38d3cfaf5ae7a922b23b8528eeed5e54d44b56b90254e

Model

witzelfitz/gardenflux:b50ad39c

yvzby9psghrm00chgjkt3q0kcr

Status

Succeeded

Source

Web

Hardware

H100

Total duration

1.6s

Created

8 months ago

Input

model: schnell
prompt: swiss garden, vegetables, flowers, swimming pool
lora_scale: 1
num_outputs: 1
aspect_ratio: 16:9
output_format: webp
guidance_scale: 3.5
output_quality: 80
extra_lora_scale: 0.8
num_inference_steps: 4

{
  "model": "schnell",
  "prompt": "swiss garden, vegetables, flowers, swimming pool",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "extra_lora_scale": 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";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run witzelfitz/gardenflux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "witzelfitz/gardenflux:b50ad39c082c82d614b38d3cfaf5ae7a922b23b8528eeed5e54d44b56b90254e",
  {
    input: {
      model: "schnell",
      prompt: "swiss garden, vegetables, flowers, swimming pool",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "16:9",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      extra_lora_scale: 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 witzelfitz/gardenflux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "witzelfitz/gardenflux:b50ad39c082c82d614b38d3cfaf5ae7a922b23b8528eeed5e54d44b56b90254e",
    input={
        "model": "schnell",
        "prompt": "swiss garden, vegetables, flowers, swimming pool",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "16:9",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 4
    }
)
print(output)

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

Run witzelfitz/gardenflux 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": "b50ad39c082c82d614b38d3cfaf5ae7a922b23b8528eeed5e54d44b56b90254e",
    "input": {
      "model": "schnell",
      "prompt": "swiss garden, vegetables, flowers, swimming pool",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "extra_lora_scale": 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.

Output

Generated in

1.6 seconds

Tweak it ShareReport

Prediction

witzelfitz/gardenflux:b50ad39c082c82d614b38d3cfaf5ae7a922b23b8528eeed5e54d44b56b90254e

Model

witzelfitz/gardenflux:b50ad39c

r4zm6bm9bxrm60chgjm9k4qyy0

Status

Succeeded

Source

Web

Hardware

H100

Total duration

10.5s

Created

8 months ago

Input

model: schnell
prompt: swiss garden, vegetables, flowers, pond
lora_scale: 1
num_outputs: 1
aspect_ratio: 16:9
output_format: webp
guidance_scale: 3.5
output_quality: 80
extra_lora_scale: 0.8
num_inference_steps: 4

{
  "model": "schnell",
  "prompt": "swiss garden, vegetables, flowers, pond",
  "lora_scale": 1,
  "num_outputs": 1,
  "aspect_ratio": "16:9",
  "output_format": "webp",
  "guidance_scale": 3.5,
  "output_quality": 80,
  "extra_lora_scale": 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";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run witzelfitz/gardenflux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "witzelfitz/gardenflux:b50ad39c082c82d614b38d3cfaf5ae7a922b23b8528eeed5e54d44b56b90254e",
  {
    input: {
      model: "schnell",
      prompt: "swiss garden, vegetables, flowers, pond",
      lora_scale: 1,
      num_outputs: 1,
      aspect_ratio: "16:9",
      output_format: "webp",
      guidance_scale: 3.5,
      output_quality: 80,
      extra_lora_scale: 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 witzelfitz/gardenflux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "witzelfitz/gardenflux:b50ad39c082c82d614b38d3cfaf5ae7a922b23b8528eeed5e54d44b56b90254e",
    input={
        "model": "schnell",
        "prompt": "swiss garden, vegetables, flowers, pond",
        "lora_scale": 1,
        "num_outputs": 1,
        "aspect_ratio": "16:9",
        "output_format": "webp",
        "guidance_scale": 3.5,
        "output_quality": 80,
        "extra_lora_scale": 0.8,
        "num_inference_steps": 4
    }
)
print(output)

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

Run witzelfitz/gardenflux 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": "b50ad39c082c82d614b38d3cfaf5ae7a922b23b8528eeed5e54d44b56b90254e",
    "input": {
      "model": "schnell",
      "prompt": "swiss garden, vegetables, flowers, pond",
      "lora_scale": 1,
      "num_outputs": 1,
      "aspect_ratio": "16:9",
      "output_format": "webp",
      "guidance_scale": 3.5,
      "output_quality": 80,
      "extra_lora_scale": 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.

Output

Generated in

10.4 seconds

Tweak it ShareReport

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witzelfitz / gardenflux

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Logs (yvzby9psghrm00chgjkt3q0kcr)

Logs (r4zm6bm9bxrm60chgjm9k4qyy0)